------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  
  log type:  text
 opened on:   1 Dec 2015, 13:18:36

. 
. */ CHAPTER 2 */
. 
. */ Figure 2.1 and Figure 2.2
. use "WhyLeadersFightLEADDataset.dta"
(LEAD Dataset v1.3 - For Why Leaders Fight)

. 
. sort ccode year

. gen yr=year(startdate)
(71 missing values generated)

. order yr

. gen decade1870=0

. replace decade1870=1 if inyear>=1870 & inyear<1880
(592 real changes made)

. gen decade1880=0

. replace decade1880=1 if inyear>=1880 & inyear<1890
(654 real changes made)

. gen decade1890=0

. replace decade1890=1 if inyear>=1890 & inyear<1900
(558 real changes made)

. gen decade1900=0

. replace decade1900=1 if inyear>=1900 & inyear<1910
(583 real changes made)

. gen decade1910=0

. replace decade1910=1 if inyear>=1910 & inyear<1920
(772 real changes made)

. gen decade1920=0

. replace decade1920=1 if inyear>=1920 & inyear<1930
(991 real changes made)

. gen decade1930=0

. replace decade1930=1 if inyear>=1930 & inyear<1940
(962 real changes made)

. gen decade1940=0

. replace decade1940=1 if inyear>=1940 & inyear<1950
(1109 real changes made)

. gen decade1950=0

. replace decade1950=1 if inyear>=1950 & inyear<1960
(1359 real changes made)

. gen decade1960=0

. replace decade1960=1 if inyear>=1960 & inyear<1970
(1966 real changes made)

. gen decade1970=0

. replace decade1970=1 if inyear>=1970 & inyear<1980
(1571 real changes made)

. gen decade1980=0

. replace decade1980=1 if inyear>=1980 & inyear<1990
(1417 real changes made)

. gen decade1990=0

. replace decade1990=1 if inyear>=1990 & inyear<2000
(1741 real changes made)

. gen decade2000=0

. replace decade2000=1 if inyear>=2000 & inyear<2010
(383 real changes made)

. gen decade=.
(15454 missing values generated)

. replace decade=1 if decade1870==1
(592 real changes made)

. replace decade=2 if decade1880==1
(654 real changes made)

. replace decade=3 if decade1890==1
(558 real changes made)

. replace decade=4 if decade1900==1
(583 real changes made)

. replace decade=5 if decade1910==1
(772 real changes made)

. replace decade=6 if decade1920==1
(991 real changes made)

. replace decade=7 if decade1930==1
(962 real changes made)

. replace decade=8 if decade1940==1
(1109 real changes made)

. replace decade=9 if decade1950==1
(1359 real changes made)

. replace decade=10 if decade1960==1
(1966 real changes made)

. replace decade=11 if decade1970==1
(1571 real changes made)

. replace decade=12 if decade1980==1
(1417 real changes made)

. replace decade=13 if decade1990==1
(1741 real changes made)

. replace decade=14 if decade2000==1
(383 real changes made)

. 
. label define decades 1 "1870s", add

. label define decades 2 "1880s", add

. label define decades 3 "1890s", add

. label define decades 4 "1900s", add

. label define decades 5 "1910s", add

. label define decades 6 "1920s", add

. label define decades 7 "1930s", add

. label define decades 8 "1940s", add

. label define decades 9 "1950s", add

. label define decades 10 "1960s", add

. label define decades 11 "1970s", add

. label define decades 12 "1980s", add

. label define decades 13 "1990s", add

. label define decades 14 "2000s", add

. label values decade decades

. drop if decade==.
(796 observations deleted)

. 
. collapse (mean) milservice milnoncombat rebel combat (median) age, by(decade)

. set scheme s1mono

. twoway (line milservice decade, lwidth(medthick)), ylabel(0 "0%" .2 "20%" .4 "40%" .6 "60%") xtitle(Decade, size(medsm
> all) margin(medsmall)) ytitle(Percentage of Leaders, size(medsmall) margin(medsmall)) graphregion(fcolor(white) lcolor
> (white) ilcolor(white)) plotregion(fcolor(white) lcolor(white) ilcolor(white)) xlabel(#14, labels labsize(small) labga
> p(small) valuelabel)

. graph save Graph "ReplicationFigure2_1.gph", replace
(file ReplicationFigure2_1.gph saved)

. 
. twoway (line age decade, lwidth(medthick)), xtitle(Decade, size(medsmall) margin(medsmall)) ytitle(Median Age of Leade
> rs, size(medsmall) margin(medsmall)) graphregion(fcolor(white) lcolor(white) ilcolor(white)) plotregion(fcolor(white) 
> lcolor(white) ilcolor(white)) xlabel(#14, labels labsize(small) labgap(small) valuelabel)

. graph save Graph "ReplicationFigure2_2.gph", replace
(file ReplicationFigure2_2.gph saved)

. 
. clear

. 
. */ Table 2.2 and Table 2.3 */
. 
. use WhyLeadersFightMonadicReplication.dta, clear
(Why Leaders Fight - Monadic Replication)

. 
. */ Initial leader risk score */
. 
. logit cwinit milnoncombat combat rebel warwin warloss rebelwin rebelloss leveledu age teacher journalism law medicine 
> religion activist careerpolitician creative business aristocratlandowner police militarycareer scienceeng bluecollar g
> ender totalspouses married marriedinpower divorced childtotal parstability illegit royalty orphanbinary officetenure10
> 00 yearssincemidinit y2 y3, robust cluster(leaderid)

Iteration 0:   log pseudolikelihood = -5579.3482  
Iteration 1:   log pseudolikelihood = -4655.1691  
Iteration 2:   log pseudolikelihood = -4544.2096  
Iteration 3:   log pseudolikelihood = -4542.1008  
Iteration 4:   log pseudolikelihood = -4542.0842  
Iteration 5:   log pseudolikelihood = -4542.0842  

Logistic regression                               Number of obs   =      11518
                                                  Wald chi2(37)   =     591.29
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -4542.0842                 Pseudo R2       =     0.1859

                                   (Std. Err. adjusted for 2258 clusters in leaderid)
-------------------------------------------------------------------------------------
                    |               Robust
             cwinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
       milnoncombat |   .7086517   .1923578     3.68   0.000     .3316373    1.085666
             combat |   .4141407   .1701076     2.43   0.015     .0807359    .7475454
              rebel |   .0221961   .1299026     0.17   0.864    -.2324082    .2768005
             warwin |   .3218007   .1647528     1.95   0.051    -.0011088    .6447103
            warloss |   .0969283   .1696773     0.57   0.568    -.2356331    .4294898
           rebelwin |  -.0605395   .1510243    -0.40   0.689    -.3565416    .2354626
          rebelloss |   .7141976    .265052     2.69   0.007     .1947052     1.23369
           leveledu |   .0462606   .0663042     0.70   0.485    -.0836932    .1762144
                age |   .0102839   .0042992     2.39   0.017     .0018576    .0187102
            teacher |  -.0202248   .1296081    -0.16   0.876    -.2742519    .2338024
         journalism |  -.1359718   .1981908    -0.69   0.493    -.5244188    .2524751
                law |  -.1464982   .1310189    -1.12   0.264    -.4032904    .1102941
           medicine |  -.5529008   .2598877    -2.13   0.033    -1.062271   -.0435302
           religion |   .3620216   .4594624     0.79   0.431    -.5385081    1.262551
           activist |   .1473004   .1300168     1.13   0.257    -.1075279    .4021287
   careerpolitician |   -.057577   .1027884    -0.56   0.575    -.2590386    .1438846
           creative |    .524738   .2454673     2.14   0.033      .043631    1.005845
           business |  -.1006797   .1429239    -0.70   0.481    -.3808055     .179446
aristocratlandowner |  -.2511304   .2122983    -1.18   0.237    -.6672273    .1649666
             police |   .2307327   .4206547     0.55   0.583    -.5937354    1.055201
     militarycareer |   -.302673   .1919031    -1.58   0.115    -.6787962    .0734502
         scienceeng |   .2175702   .2413567     0.90   0.367    -.2554802    .6906205
         bluecollar |  -.0453714   .2007014    -0.23   0.821    -.4387389    .3479961
             gender |  -.3938837   .2863857    -1.38   0.169    -.9551893    .1674218
       totalspouses |  -.0202293   .0185804    -1.09   0.276    -.0566462    .0161875
            married |   .0396029   .3526166     0.11   0.911    -.6515128    .7307187
     marriedinpower |  -.1747878   .1984453    -0.88   0.378    -.5637335    .2141579
           divorced |   -.016662   .1196519    -0.14   0.889    -.2511755    .2178515
         childtotal |   .0039692   .0054605     0.73   0.467    -.0067332    .0146717
       parstability |   .3783283   .1988239     1.90   0.057    -.0113595    .7680161
            illegit |  -.5236266   .2319828    -2.26   0.024    -.9783045   -.0689486
            royalty |  -.2141826   .2088862    -1.03   0.305    -.6235919    .1952268
       orphanbinary |  -.0978384    .257739    -0.38   0.704    -.6029976    .4073208
   officetenure1000 |   .0243156   .0145865     1.67   0.096    -.0042734    .0529046
  yearssincemidinit |  -.3304432   .0229776   -14.38   0.000    -.3754785    -.285408
                 y2 |   .0108669   .0013562     8.01   0.000     .0082088    .0135249
                 y3 |   -.000099   .0000192    -5.14   0.000    -.0001367   -.0000612
              _cons |  -.7621431   .5220308    -1.46   0.144    -1.785305    .2610184
-------------------------------------------------------------------------------------

. predict leaderrisk if e(sample)
(option pr assumed; Pr(cwinit))
(52 missing values generated)

. la var leaderrisk "Leader Attribute Risk Score"

. 
. */ Initial system risk score */
. 
. logit cwinit cinc dem aut syscon irregular tau_lead fiveyearchallengelag lastwarwin lastwarloss lastwardraw yearssince
> midinit y2 y3, robust cluster(ccode)

Iteration 0:   log pseudolikelihood =  -5505.478  
Iteration 1:   log pseudolikelihood = -4388.0857  
Iteration 2:   log pseudolikelihood = -4239.6206  
Iteration 3:   log pseudolikelihood = -4230.9585  
Iteration 4:   log pseudolikelihood = -4230.8574  
Iteration 5:   log pseudolikelihood = -4230.8571  

Logistic regression                               Number of obs   =      11388
                                                  Wald chi2(13)   =     785.87
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -4230.8571                 Pseudo R2       =     0.2315

                                        (Std. Err. adjusted for 178 clusters in ccode)
--------------------------------------------------------------------------------------
                     |               Robust
              cwinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
                cinc |   9.404467   2.027024     4.64   0.000     5.431572    13.37736
                 dem |  -.2396804   .1588788    -1.51   0.131     -.551077    .0717163
                 aut |   .1679045   .1299729     1.29   0.196    -.0868377    .4226466
              syscon |  -3.486719   1.066383    -3.27   0.001    -5.576791   -1.396647
           irregular |   .2121851   .1162016     1.83   0.068    -.0155659    .4399361
            tau_lead |  -.0436213   .1617221    -0.27   0.787    -.3605907    .2733482
fiveyearchallengelag |   .6592767   .0783817     8.41   0.000     .5056514     .812902
          lastwarwin |   .7341308   .1532903     4.79   0.000     .4336873    1.034574
         lastwarloss |   .6475341   .1319239     4.91   0.000      .388968    .9061001
         lastwardraw |    .929418    .211505     4.39   0.000     .5148757     1.34396
   yearssincemidinit |  -.2690812   .0237309   -11.34   0.000    -.3155929   -.2225696
                  y2 |   .0092909    .001325     7.01   0.000     .0066941    .0118878
                  y3 |  -.0000889    .000018    -4.94   0.000    -.0001242   -.0000536
               _cons |  -.7252537   .3385474    -2.14   0.032    -1.388794   -.0617129
--------------------------------------------------------------------------------------

. predict systemrisk if e(sample)
(option pr assumed; Pr(cwinit))
(182 missing values generated)

. la var systemrisk "System Risk Score"

. 
. */ generate MID initiation summary data for creation upon data collapse */
. gen cwinit_sum = cwinit

. la var cwinit_sum "Number of MID Initiations"

. 
. collapse (mean) leaderrisk systemrisk (sum) cwinit_sum (min) ccode inyear (max) outyear, by(leaderid leadername idacr)

. 
. */ List top 35 conflict prone leaders, by leader risk score */
. gsort -leaderrisk

. 
. list ccode idacr leaderid leadername leaderrisk systemrisk cwinit_sum in 1/35, table clean noobs

    ccode   idacr       leaderid             leadername   leader~k   system~k   cwinit~m  
      710     CHN   LEAD.v1-7855          Deng Xiaoping   .6918895   .7410197         16  
      630     IRN   LEAD.v1-7252     Ayatollah Khomeini   .6917507   .5692552         64  
        2     USA     LEAD.v1-67          Ronald Reagan    .684314   .6556708         16  
      344     CRO   LEAD.v1-4975         Franjo Tudjman   .6645699   .2393584          5  
      220     FRN   LEAD.v1-3532        Georges Bidault    .644871   .3106591          0  
      710     CHN   LEAD.v1-7849             Mao Zedong   .6333759   .7220504         45  
      560     SAF   LEAD.v1-6904              Jan Smuts   .6267354   .0971961          0  
      645     IRQ   LEAD.v1-7393         Hassan Al-Bakr   .6092224   .4540481          8  
        2     USA     LEAD.v1-52        John F. Kennedy   .6027604   .8063945          2  
        2     USA     LEAD.v1-61            Gerald Ford   .5843109   .6619125          4  
        2     USA     LEAD.v1-25     Theodore Roosevelt   .5829648   .6240175          7  
      135     PER   LEAD.v1-1843             A. Caceres   .5780718   .2388555          0  
      365     RUS   LEAD.v1-5491   Konstantin Chernenko   .5636779   .8005411          2  
      710     CHN   LEAD.v1-7852            Hua Guofeng   .5632941   .7426027          4  
      220     FRN   LEAD.v1-3592               Gaillard   .5591558   .3012592          1  
      365     RUS   LEAD.v1-5476           Josef Stalin   .5565562   .6835461         38  
      366     EST   LEAD.v1-5503                   Pats   .5490516   .2161491          0  
        2     USA      LEAD.v1-7               Garfield   .5490223   .4602128          0  
      220     FRN   LEAD.v1-3589        Bourges-Maunory   .5470842   .4475837          0  
      365     RUS   LEAD.v1-5482      Nikita Khrushchev   .5404414   .7972519         26  
      666     ISR   LEAD.v1-7531             Ben Gurion    .533806   .2887749          7  
      365     RUS   LEAD.v1-5485        Leonid Brezhnev   .5316504   .8114074         31  
      666     ISR   LEAD.v1-7540                 Eshkol   .5251138   .3292592          4  
        2     USA     LEAD.v1-70                   Bush   .5239335   .7060421          5  
      255     GMY   LEAD.v1-4354           Adolf Hitler    .519618   .7174438         52  
      350     GRC   LEAD.v1-5203              Sophoulis   .5159904   .2160157          1  
      666     ISR   LEAD.v1-7537             Ben Gurion   .5059983    .302938          4  
      350     GRC   LEAD.v1-5215              Plastiras   .5030751   .2181742          1  
      200     UKG   LEAD.v1-2875                  Heath   .4998602   .4316876          1  
      367     LAT   LEAD.v1-5593                Ulmanis   .4978945    .254904          0  
      645     IRQ   LEAD.v1-7396         Saddam Hussein   .4914221   .5062273         44  
      750     IND   LEAD.v1-8254                 Gujral   .4886026    .497023          0  
      666     ISR   LEAD.v1-7534                Sharett   .4864107   .2900691          3  
      750     IND   LEAD.v1-8242                Shekhar   .4863379   .5191203          1  
      680     YPR   LEAD.v1-7636                 Ismail   .4796922   .3142383          0  

. 
. */ List top 35 conflict prone leaders, by system risk score */
. 
. gsort -systemrisk

. 
. list ccode idacr leaderid leadername leaderrisk systemrisk cwinit_sum in 1/35, table clean noobs

    ccode   idacr       leaderid                 leadername   leader~k   system~k   cwinit~m  
        2     USA     LEAD.v1-49          Dwight Eisenhower   .4548808   .8323486         10  
      365     RUS   LEAD.v1-5488              Yuri Andropov   .3909119   .8212816          4  
      710     CHN   LEAD.v1-7858                Jiang Zemin   .3666206   .8165689          8  
      365     RUS   LEAD.v1-5485            Leonid Brezhnev   .5316504   .8114074         31  
        2     USA     LEAD.v1-34                    Harding   .2717349   .8090542          1  
        2     USA     LEAD.v1-52            John F. Kennedy   .6027604   .8063945          2  
      365     RUS   LEAD.v1-5491       Konstantin Chernenko   .5636779   .8005411          2  
      365     RUS   LEAD.v1-5482          Nikita Khrushchev   .5404414   .7972519         26  
      200     UKG   LEAD.v1-2803   Salisbury (3rd Marquess)   .3199288   .7853059          1  
        2     USA     LEAD.v1-31             Woodrow Wilson   .3534467   .7816476         10  
        2     USA     LEAD.v1-55             Lyndon Johnson   .4745872   .7782919          5  
        2     USA     LEAD.v1-46                     Truman   .3341923   .7745753          1  
      255     GMY   LEAD.v1-4309                      Ebert   .2995855   .7530078          0  
      365     RUS   LEAD.v1-5494          Mikhail Gorbachev   .2743491   .7467705          4  
      710     CHN   LEAD.v1-7852                Hua Guofeng   .5632941   .7426027          4  
      710     CHN   LEAD.v1-7855              Deng Xiaoping   .6918895   .7410197         16  
      200     UKG   LEAD.v1-2806                  Gladstone    .393115   .7381127          1  
        2     USA     LEAD.v1-22                   McKinley   .4595375   .7367111          3  
      200     UKG   LEAD.v1-2800                  Gladstone   .3914341   .7325172          5  
      710     CHN   LEAD.v1-7849                 Mao Zedong   .6333759   .7220504         45  
      200     UKG   LEAD.v1-2809   Salisbury (3rd Marquess)   .3078676   .7200394          5  
      255     GMY   LEAD.v1-4354               Adolf Hitler    .519618   .7174438         52  
        2     USA     LEAD.v1-73            William Clinton   .2976701   .7068924         15  
        2     USA     LEAD.v1-70                       Bush   .5239335   .7060421          5  
      200     UKG   LEAD.v1-2818   Salisbury (3rd Marquess)   .3444588   .6977786         10  
        2     USA     LEAD.v1-58                      Nixon   .4733009   .6928139          4  
      365     RUS   LEAD.v1-5476               Josef Stalin   .5565562   .6835461         38  
        2     USA     LEAD.v1-28                       Taft   .3028729   .6687551          5  
        2     USA     LEAD.v1-61                Gerald Ford   .5843109   .6619125          4  
        2     USA     LEAD.v1-67              Ronald Reagan    .684314   .6556708         16  
      200     UKG   LEAD.v1-2812                  Gladstone   .3722841   .6527191          0  
        2     USA     LEAD.v1-16                   Harrison   .3551699   .6514959          3  
        2     USA     LEAD.v1-64                     Carter   .4680297   .6420552          4  
        2     USA     LEAD.v1-43              Roosevelt, F.   .2094903   .6373102          2  
      710     CHN   LEAD.v1-7834            Chiang Kai-shek   .3576236   .6310818          3  

. 
. */ List top 2% of most dangerous leaders in reality, and their military dispute initiations, comparing how leader mode
> l v. system model predict their risk % */
. gsort -leaderid

. gsort -cwinit_sum

. 
. xtile pct = leaderrisk, nq(100)

. xtile pct2 = systemrisk, nq(100)

. xtile pct3 = cwinit_sum, nq(100)

. replace pct=pct-1
(2258 real changes made)

. replace pct2=pct2-1
(2258 real changes made)

. replace pct3=pct3-1
(2282 real changes made)

. list ccode idacr leaderid leadername pct pct2 pct3 cwinit_sum if pct3>=98, table clean noobs

    ccode   idacr       leaderid                 leadername   pct   pct2   pct3   cwinit~m  
      630     IRN   LEAD.v1-7252         Ayatollah Khomeini    99     97     99         64  
      255     GMY   LEAD.v1-4354               Adolf Hitler    98     99     99         52  
      710     CHN   LEAD.v1-7849                 Mao Zedong    99     99     99         45  
      645     IRQ   LEAD.v1-7396             Saddam Hussein    98     96     99         44  
      365     RUS   LEAD.v1-5476               Josef Stalin    99     98     99         38  
      365     RUS   LEAD.v1-5485            Leonid Brezhnev    99     99     99         31  
      325     ITA   LEAD.v1-4720           Benito Mussolini    98     93     99         27  
      365     RUS   LEAD.v1-5482          Nikita Khrushchev    99     99     99         26  
      255     GMY   LEAD.v1-4306                 Wilhelm II    94     96     99         25  
      365     RUS   LEAD.v1-5470                Nicholas II    95     97     99         24  
      365     RUS   LEAD.v1-5497              Boris Yeltsin    89     97     99         23  
      731     PRK   LEAD.v1-7942                Kim Il-Sung    97     90     99         21  
      490     DRC   LEAD.v1-6703           Mobutu Sese Seko    73     88     99         17  
      713     TAW   LEAD.v1-7921            Chiang Kai-shek    94     83     99         17  
      750     IND   LEAD.v1-8212           Jawaharlal Nehru    91     92     99         17  
      710     CHN   LEAD.v1-7855              Deng Xiaoping    99     99     99         16  
      652     SYR   LEAD.v1-7474             Hafez Al-Assad    94     89     99         16  
        2     USA     LEAD.v1-67              Ronald Reagan    99     98     99         16  
      620     LIB   LEAD.v1-7186            Muammar Qaddafi    90     88     99         16  
        2     USA     LEAD.v1-73            William Clinton    81     98     99         15  
      305     AUH   LEAD.v1-4438           Francis Joseph I    87     76     98         13  
      651     EGY   LEAD.v1-7420         Gamal Abdel Nasser    98     94     98         13  
      770     PAK   LEAD.v1-8317                  Ayub Khan    96     96     98         13  
      600     MOR   LEAD.v1-7138                  Hassan II    87     79     98         13  
      850     INS   LEAD.v1-8860                    Sukarno    92     57     98         13  
      560     SAF   LEAD.v1-6931                Louis Botha    86     89     98         12  
      530     ETH   LEAD.v1-6850           Mengistu Marriam    90     89     98         12  
      552     ZIM   LEAD.v1-6883                  Ian Smith    86     73     98         12  
      345     YUG   LEAD.v1-5029         Slobodan Milosevic    92     89     98         11  
      500     UGA   LEAD.v1-6715                   Idi Amin    96     85     98         11  
        2     USA     LEAD.v1-49          Dwight Eisenhower    96     99     98         10  
      220     FRN   LEAD.v1-3613         Francois Mitterand    94     89     98         10  
      365     RUS   LEAD.v1-5473             Vladimir Lenin    82     98     98         10  
      200     UKG   LEAD.v1-2818   Salisbury (3rd Marquess)    88     98     98         10  
      200     UKG   LEAD.v1-2851        Neville Chamberlain    82     95     98         10  
        2     USA     LEAD.v1-31             Woodrow Wilson    89     99     98         10  
      235     POR   LEAD.v1-4279             Caetano Veloso    85     94     98          9  
       40     CUB    LEAD.v1-211               Fidel Castro    59     79     98          9  
      652     SYR   LEAD.v1-7438             Abid Shishakli    91     90     98          9  
      200     UKG   LEAD.v1-2854          Winston Churchill    96     96     98          9  
      630     IRN   LEAD.v1-7258   Akbar Hashemi Rafsanjani    89     97     98          9  
      640     TUR   LEAD.v1-7336                Turgut Ozal    91     91     98          9  

. 
. clear

. 
. use WhyLeadersFightMonadicReplication.dta
(Why Leaders Fight - Monadic Replication)

. 
. */ Figure 2.3 */
. 
. logit cwinit medicine illegit gender militarycareer aristocratlandowner marriedinpower royalty law journalism business
>  orphanbinary rebelwin divorced careerpolitician bluecollar totalspouses teacher age childtotal rebel married leveledu
>  warloss activist scienceeng police warwin religion parstability combat creative milnoncombat rebelloss officetenure10
> 00 yearssincemidinit y2 y3, robust cluster(leaderid)

Iteration 0:   log pseudolikelihood = -5579.3482  
Iteration 1:   log pseudolikelihood = -4655.1691  
Iteration 2:   log pseudolikelihood = -4544.2096  
Iteration 3:   log pseudolikelihood = -4542.1008  
Iteration 4:   log pseudolikelihood = -4542.0842  
Iteration 5:   log pseudolikelihood = -4542.0842  

Logistic regression                               Number of obs   =      11518
                                                  Wald chi2(37)   =     591.29
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -4542.0842                 Pseudo R2       =     0.1859

                                   (Std. Err. adjusted for 2258 clusters in leaderid)
-------------------------------------------------------------------------------------
                    |               Robust
             cwinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
           medicine |  -.5529008   .2598877    -2.13   0.033    -1.062271   -.0435302
            illegit |  -.5236266   .2319828    -2.26   0.024    -.9783045   -.0689486
             gender |  -.3938837   .2863857    -1.38   0.169    -.9551893    .1674218
     militarycareer |   -.302673   .1919031    -1.58   0.115    -.6787962    .0734502
aristocratlandowner |  -.2511304   .2122983    -1.18   0.237    -.6672273    .1649666
     marriedinpower |  -.1747878   .1984453    -0.88   0.378    -.5637335    .2141579
            royalty |  -.2141826   .2088862    -1.03   0.305    -.6235919    .1952268
                law |  -.1464982   .1310189    -1.12   0.264    -.4032904    .1102941
         journalism |  -.1359718   .1981908    -0.69   0.493    -.5244188    .2524751
           business |  -.1006797   .1429239    -0.70   0.481    -.3808055     .179446
       orphanbinary |  -.0978384    .257739    -0.38   0.704    -.6029976    .4073208
           rebelwin |  -.0605395   .1510243    -0.40   0.689    -.3565416    .2354626
           divorced |   -.016662   .1196519    -0.14   0.889    -.2511755    .2178515
   careerpolitician |   -.057577   .1027884    -0.56   0.575    -.2590386    .1438846
         bluecollar |  -.0453714   .2007014    -0.23   0.821    -.4387389    .3479961
       totalspouses |  -.0202293   .0185804    -1.09   0.276    -.0566462    .0161875
            teacher |  -.0202248   .1296081    -0.16   0.876    -.2742519    .2338024
                age |   .0102839   .0042992     2.39   0.017     .0018576    .0187102
         childtotal |   .0039692   .0054605     0.73   0.467    -.0067332    .0146717
              rebel |   .0221961   .1299026     0.17   0.864    -.2324082    .2768005
            married |   .0396029   .3526166     0.11   0.911    -.6515128    .7307187
           leveledu |   .0462606   .0663042     0.70   0.485    -.0836932    .1762144
            warloss |   .0969283   .1696773     0.57   0.568    -.2356331    .4294898
           activist |   .1473004   .1300168     1.13   0.257    -.1075279    .4021287
         scienceeng |   .2175702   .2413567     0.90   0.367    -.2554802    .6906205
             police |   .2307327   .4206547     0.55   0.583    -.5937354    1.055201
             warwin |   .3218007   .1647528     1.95   0.051    -.0011088    .6447103
           religion |   .3620216   .4594624     0.79   0.431    -.5385081    1.262551
       parstability |   .3783283   .1988239     1.90   0.057    -.0113595    .7680161
             combat |   .4141407   .1701076     2.43   0.015     .0807359    .7475454
           creative |    .524738   .2454673     2.14   0.033      .043631    1.005845
       milnoncombat |   .7086517   .1923578     3.68   0.000     .3316373    1.085666
          rebelloss |   .7141976    .265052     2.69   0.007     .1947052     1.23369
   officetenure1000 |   .0243156   .0145865     1.67   0.096    -.0042734    .0529046
  yearssincemidinit |  -.3304432   .0229776   -14.38   0.000    -.3754785    -.285408
                 y2 |   .0108669   .0013562     8.01   0.000     .0082088    .0135249
                 y3 |   -.000099   .0000192    -5.14   0.000    -.0001367   -.0000612
              _cons |  -.7621431   .5220308    -1.46   0.144    -1.785305    .2610184
-------------------------------------------------------------------------------------

. 
. predict leaderrisk if e(sample)
(option pr assumed; Pr(cwinit))
(52 missing values generated)

. 
. estimates store m1

. 
. coefplot m1, keep(milnoncombat combat rebel warwin warloss rebelwin rebelloss leveledu age teacher journalism law medi
> cine religion activist careerpolitician creative business aristocratlandowner police militarycareer scienceeng bluecol
> lar gender totalspouses married marriedinpower divorced childtotal parstability illegit royalty orphanbinary) scheme(s
> 1mono) rename(milnoncombat = "Military Service, No Combat" combat = "Military Service, Combat" rebel = "Rebel Service"
>  warwin = "Prior War Win" warloss = "Prior War Loss" rebelwin = "Prior Rebel Win" rebelloss = "Prior Rebel Loss" level
> edu = "Level of Education" age = "Age" teacher = "Occupation: Teacher" journalism = "Occupation: Journalism" law = "Oc
> cupation: Law" medicine = "Occupation: Medicine" religion = "Occupation: Religion" activist = "Occupation: Activist" c
> areerpolitician = "Occupation: Career Politician" creative = "Occupation: Creative" business = "Occupation: Business C
> areer" aristocratlandowner = "Occupation: Aristocrat" police = "Occupation: Police" militarycareer = "Occupation: Mili
> tary Career" scienceeng = "Occupation: Science" bluecollar = "Occupation: Blue Collar" gender = "Gender" totalspouses 
> = "Number of Spouses" married = "Married (Ever)" marriedinpower = "Married in Power" divorced = "Divorced" childtotal 
> = "Number of Children" parstability = "Parental Stability" illegit = "Considered 'Illegitimate' Child" royalty = "Roya
> lty" orphanbinary = "Orphan") grid(none) order(model1) scale(.75) aspectratio(1.4) xline(0)

. 
. graph save Graph "ReplicationFigure2_3.gph", replace
(file ReplicationFigure2_3.gph saved)

. 
. clear

. 
. */ CHAPTER 3 */
. 
. use WhyLeadersFightMonadicReplication.dta
(Why Leaders Fight - Monadic Replication)

. 
. */ NOTE: The maps in Why Leaders Fight were created by using the same leader risk model presented in Figure 2.3, then 
> plotting the risk scores for those leaders on maps in different time periods */
. */ NOTE: maps created using cshapes and the spmap command */
. 
. */ Figure 3.3 */
. logit cwinit milnoncombat combat rebel warwin warloss rebelwin rebelloss leveledu age teacher journalism law medicine 
> religion activist careerpolitician creative business aristocratlandowner police militarycareer scienceeng bluecollar g
> ender totalspouses married marriedinpower divorced childtotal parstability illegit royalty orphanbinary officetenure10
> 00 yearssincemidinit y2 y3, robust cluster(leaderid)

Iteration 0:   log pseudolikelihood = -5579.3482  
Iteration 1:   log pseudolikelihood = -4655.1691  
Iteration 2:   log pseudolikelihood = -4544.2096  
Iteration 3:   log pseudolikelihood = -4542.1008  
Iteration 4:   log pseudolikelihood = -4542.0842  
Iteration 5:   log pseudolikelihood = -4542.0842  

Logistic regression                               Number of obs   =      11518
                                                  Wald chi2(37)   =     591.29
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -4542.0842                 Pseudo R2       =     0.1859

                                   (Std. Err. adjusted for 2258 clusters in leaderid)
-------------------------------------------------------------------------------------
                    |               Robust
             cwinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
       milnoncombat |   .7086517   .1923578     3.68   0.000     .3316373    1.085666
             combat |   .4141407   .1701076     2.43   0.015     .0807359    .7475454
              rebel |   .0221961   .1299026     0.17   0.864    -.2324082    .2768005
             warwin |   .3218007   .1647528     1.95   0.051    -.0011088    .6447103
            warloss |   .0969283   .1696773     0.57   0.568    -.2356331    .4294898
           rebelwin |  -.0605395   .1510243    -0.40   0.689    -.3565416    .2354626
          rebelloss |   .7141976    .265052     2.69   0.007     .1947052     1.23369
           leveledu |   .0462606   .0663042     0.70   0.485    -.0836932    .1762144
                age |   .0102839   .0042992     2.39   0.017     .0018576    .0187102
            teacher |  -.0202248   .1296081    -0.16   0.876    -.2742519    .2338024
         journalism |  -.1359718   .1981908    -0.69   0.493    -.5244188    .2524751
                law |  -.1464982   .1310189    -1.12   0.264    -.4032904    .1102941
           medicine |  -.5529008   .2598877    -2.13   0.033    -1.062271   -.0435302
           religion |   .3620216   .4594624     0.79   0.431    -.5385081    1.262551
           activist |   .1473004   .1300168     1.13   0.257    -.1075279    .4021287
   careerpolitician |   -.057577   .1027884    -0.56   0.575    -.2590386    .1438846
           creative |    .524738   .2454673     2.14   0.033      .043631    1.005845
           business |  -.1006797   .1429239    -0.70   0.481    -.3808055     .179446
aristocratlandowner |  -.2511304   .2122983    -1.18   0.237    -.6672273    .1649666
             police |   .2307327   .4206547     0.55   0.583    -.5937354    1.055201
     militarycareer |   -.302673   .1919031    -1.58   0.115    -.6787962    .0734502
         scienceeng |   .2175702   .2413567     0.90   0.367    -.2554802    .6906205
         bluecollar |  -.0453714   .2007014    -0.23   0.821    -.4387389    .3479961
             gender |  -.3938837   .2863857    -1.38   0.169    -.9551893    .1674218
       totalspouses |  -.0202293   .0185804    -1.09   0.276    -.0566462    .0161875
            married |   .0396029   .3526166     0.11   0.911    -.6515128    .7307187
     marriedinpower |  -.1747878   .1984453    -0.88   0.378    -.5637335    .2141579
           divorced |   -.016662   .1196519    -0.14   0.889    -.2511755    .2178515
         childtotal |   .0039692   .0054605     0.73   0.467    -.0067332    .0146717
       parstability |   .3783283   .1988239     1.90   0.057    -.0113595    .7680161
            illegit |  -.5236266   .2319828    -2.26   0.024    -.9783045   -.0689486
            royalty |  -.2141826   .2088862    -1.03   0.305    -.6235919    .1952268
       orphanbinary |  -.0978384    .257739    -0.38   0.704    -.6029976    .4073208
   officetenure1000 |   .0243156   .0145865     1.67   0.096    -.0042734    .0529046
  yearssincemidinit |  -.3304432   .0229776   -14.38   0.000    -.3754785    -.285408
                 y2 |   .0108669   .0013562     8.01   0.000     .0082088    .0135249
                 y3 |   -.000099   .0000192    -5.14   0.000    -.0001367   -.0000612
              _cons |  -.7621431   .5220308    -1.46   0.144    -1.785305    .2610184
-------------------------------------------------------------------------------------

. predict leaderrisk if e(sample)
(option pr assumed; Pr(cwinit))
(52 missing values generated)

. 
. gen region="North America" if ccode>=0

. replace region="Central America" if ccode>20
region was str13 now str15
(11305 real changes made)

. replace region="Americas" if ccode>100
(9933 real changes made)

. replace region="Europe" if ccode>200
(8501 real changes made)

. replace region="Africa" if ccode>400
(5220 real changes made)

. replace region="Middle East" if ccode>600
(3279 real changes made)

. replace region="Asia" if ccode>700
(1861 real changes made)

. 
. collapse (mean) leaderrisk, by(year)

. sort leaderrisk

. sort year

. 
. twoway line leaderrisk year || qfit leaderrisk year, lp(dash) legend(label(1 "Mean Risk Score") label(2 "Quadratic Fit
> ")) xlabel(#10) ytitle(Leader Risk Score) 

. 
. graph save Graph "ReplicationFigure3_3.gph", replace
(file ReplicationFigure3_3.gph saved)

. 
. */ Figure 3.4 */
. 
. clear

. 
. use WhyLeadersFightMonadicReplication.dta
(Why Leaders Fight - Monadic Replication)

. 
. logit cwinit milnoncombat combat rebel warwin warloss rebelwin rebelloss leveledu age teacher journalism law medicine 
> religion activist careerpolitician creative business aristocratlandowner police militarycareer scienceeng bluecollar g
> ender totalspouses married marriedinpower divorced childtotal parstability illegit royalty orphanbinary officetenure10
> 00 yearssincemidinit y2 y3, robust cluster(leaderid)

Iteration 0:   log pseudolikelihood = -5579.3482  
Iteration 1:   log pseudolikelihood = -4655.1691  
Iteration 2:   log pseudolikelihood = -4544.2096  
Iteration 3:   log pseudolikelihood = -4542.1008  
Iteration 4:   log pseudolikelihood = -4542.0842  
Iteration 5:   log pseudolikelihood = -4542.0842  

Logistic regression                               Number of obs   =      11518
                                                  Wald chi2(37)   =     591.29
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -4542.0842                 Pseudo R2       =     0.1859

                                   (Std. Err. adjusted for 2258 clusters in leaderid)
-------------------------------------------------------------------------------------
                    |               Robust
             cwinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
       milnoncombat |   .7086517   .1923578     3.68   0.000     .3316373    1.085666
             combat |   .4141407   .1701076     2.43   0.015     .0807359    .7475454
              rebel |   .0221961   .1299026     0.17   0.864    -.2324082    .2768005
             warwin |   .3218007   .1647528     1.95   0.051    -.0011088    .6447103
            warloss |   .0969283   .1696773     0.57   0.568    -.2356331    .4294898
           rebelwin |  -.0605395   .1510243    -0.40   0.689    -.3565416    .2354626
          rebelloss |   .7141976    .265052     2.69   0.007     .1947052     1.23369
           leveledu |   .0462606   .0663042     0.70   0.485    -.0836932    .1762144
                age |   .0102839   .0042992     2.39   0.017     .0018576    .0187102
            teacher |  -.0202248   .1296081    -0.16   0.876    -.2742519    .2338024
         journalism |  -.1359718   .1981908    -0.69   0.493    -.5244188    .2524751
                law |  -.1464982   .1310189    -1.12   0.264    -.4032904    .1102941
           medicine |  -.5529008   .2598877    -2.13   0.033    -1.062271   -.0435302
           religion |   .3620216   .4594624     0.79   0.431    -.5385081    1.262551
           activist |   .1473004   .1300168     1.13   0.257    -.1075279    .4021287
   careerpolitician |   -.057577   .1027884    -0.56   0.575    -.2590386    .1438846
           creative |    .524738   .2454673     2.14   0.033      .043631    1.005845
           business |  -.1006797   .1429239    -0.70   0.481    -.3808055     .179446
aristocratlandowner |  -.2511304   .2122983    -1.18   0.237    -.6672273    .1649666
             police |   .2307327   .4206547     0.55   0.583    -.5937354    1.055201
     militarycareer |   -.302673   .1919031    -1.58   0.115    -.6787962    .0734502
         scienceeng |   .2175702   .2413567     0.90   0.367    -.2554802    .6906205
         bluecollar |  -.0453714   .2007014    -0.23   0.821    -.4387389    .3479961
             gender |  -.3938837   .2863857    -1.38   0.169    -.9551893    .1674218
       totalspouses |  -.0202293   .0185804    -1.09   0.276    -.0566462    .0161875
            married |   .0396029   .3526166     0.11   0.911    -.6515128    .7307187
     marriedinpower |  -.1747878   .1984453    -0.88   0.378    -.5637335    .2141579
           divorced |   -.016662   .1196519    -0.14   0.889    -.2511755    .2178515
         childtotal |   .0039692   .0054605     0.73   0.467    -.0067332    .0146717
       parstability |   .3783283   .1988239     1.90   0.057    -.0113595    .7680161
            illegit |  -.5236266   .2319828    -2.26   0.024    -.9783045   -.0689486
            royalty |  -.2141826   .2088862    -1.03   0.305    -.6235919    .1952268
       orphanbinary |  -.0978384    .257739    -0.38   0.704    -.6029976    .4073208
   officetenure1000 |   .0243156   .0145865     1.67   0.096    -.0042734    .0529046
  yearssincemidinit |  -.3304432   .0229776   -14.38   0.000    -.3754785    -.285408
                 y2 |   .0108669   .0013562     8.01   0.000     .0082088    .0135249
                 y3 |   -.000099   .0000192    -5.14   0.000    -.0001367   -.0000612
              _cons |  -.7621431   .5220308    -1.46   0.144    -1.785305    .2610184
-------------------------------------------------------------------------------------

. predict leaderrisk if e(sample)
(option pr assumed; Pr(cwinit))
(52 missing values generated)

. 
. xtile leaderrisk_d = leaderrisk, nq(10)

. 
. gen region=1 if ccode>=0

. replace region=2 if ccode>20
(11305 real changes made)

. replace region=3 if ccode>=200
(8671 real changes made)

. replace region=4 if ccode>=400
(5220 real changes made)

. replace region=5 if ccode>=600
(3363 real changes made)

. replace region=6 if ccode>=700
(1944 real changes made)

. 
. label define region 1 "North America" 2 "Central/South America" 3 "Europe" 4 "Sub-Saharan Africa" 5 "Middle East/N. Af
> rica" 6 "Asia"

. 
. sort region

. by region: tab leaderrisk_d

------------------------------------------------------------------------------------------------------------------------
-> region = 1

         10 |
  quantiles |
         of |
 leaderrisk |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |          4        1.51        1.51
          2 |          4        1.51        3.02
          3 |         19        7.17       10.19
          4 |         18        6.79       16.98
          5 |         12        4.53       21.51
          6 |         18        6.79       28.30
          7 |         32       12.08       40.38
          8 |         48       18.11       58.49
          9 |         31       11.70       70.19
         10 |         79       29.81      100.00
------------+-----------------------------------
      Total |        265      100.00

------------------------------------------------------------------------------------------------------------------------
-> region = 2

         10 |
  quantiles |
         of |
 leaderrisk |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        275       10.51       10.51
          2 |        295       11.27       21.78
          3 |        331       12.65       34.43
          4 |        329       12.57       47.00
          5 |        295       11.27       58.27
          6 |        289       11.04       69.32
          7 |        281       10.74       80.05
          8 |        242        9.25       89.30
          9 |        181        6.92       96.22
         10 |         99        3.78      100.00
------------+-----------------------------------
      Total |      2,617      100.00

------------------------------------------------------------------------------------------------------------------------
-> region = 3

         10 |
  quantiles |
         of |
 leaderrisk |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        347       10.06       10.06
          2 |        339        9.83       19.89
          3 |        368       10.67       30.56
          4 |        373       10.81       41.37
          5 |        337        9.77       51.15
          6 |        325        9.42       60.57
          7 |        311        9.02       69.59
          8 |        356       10.32       79.91
          9 |        311        9.02       88.92
         10 |        382       11.08      100.00
------------+-----------------------------------
      Total |      3,449      100.00

------------------------------------------------------------------------------------------------------------------------
-> region = 4

         10 |
  quantiles |
         of |
 leaderrisk |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        164        8.85        8.85
          2 |        272       14.67       23.52
          3 |        224       12.08       35.60
          4 |        205       11.06       46.66
          5 |        206       11.11       57.77
          6 |        218       11.76       69.53
          7 |        184        9.92       79.45
          8 |        154        8.31       87.76
          9 |        147        7.93       95.69
         10 |         80        4.31      100.00
------------+-----------------------------------
      Total |      1,854      100.00

------------------------------------------------------------------------------------------------------------------------
-> region = 5

         10 |
  quantiles |
         of |
 leaderrisk |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        175       12.41       12.41
          2 |         94        6.67       19.08
          3 |         67        4.75       23.83
          4 |         81        5.74       29.57
          5 |        101        7.16       36.74
          6 |        134        9.50       46.24
          7 |        137        9.72       55.96
          8 |        141       10.00       65.96
          9 |        204       14.47       80.43
         10 |        276       19.57      100.00
------------+-----------------------------------
      Total |      1,410      100.00

------------------------------------------------------------------------------------------------------------------------
-> region = 6

         10 |
  quantiles |
         of |
 leaderrisk |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        187        9.72        9.72
          2 |        148        7.70       17.42
          3 |        143        7.44       24.86
          4 |        146        7.59       32.45
          5 |        200       10.40       42.85
          6 |        168        8.74       51.59
          7 |        207       10.76       62.35
          8 |        211       10.97       73.32
          9 |        278       14.46       87.78
         10 |        235       12.22      100.00
------------+-----------------------------------
      Total |      1,923      100.00


. 
. * We then used these totals to create the bubble chart seen in Figure 3.4 in Excel *
. 
. */ Figure 3.6 */
. 
. clear

. 
. use WhyLeadersFightMonadicReplication.dta
(Why Leaders Fight - Monadic Replication)

. 
. logit cwinit milnoncombat combat rebel warwin warloss rebelwin rebelloss leveledu age teacher journalism law medicine 
> religion activist careerpolitician creative business aristocratlandowner police militarycareer scienceeng bluecollar g
> ender totalspouses married marriedinpower divorced childtotal parstability illegit royalty orphanbinary officetenure10
> 00 yearssincemidinit y2 y3, robust cluster(leaderid)

Iteration 0:   log pseudolikelihood = -5579.3482  
Iteration 1:   log pseudolikelihood = -4655.1691  
Iteration 2:   log pseudolikelihood = -4544.2096  
Iteration 3:   log pseudolikelihood = -4542.1008  
Iteration 4:   log pseudolikelihood = -4542.0842  
Iteration 5:   log pseudolikelihood = -4542.0842  

Logistic regression                               Number of obs   =      11518
                                                  Wald chi2(37)   =     591.29
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -4542.0842                 Pseudo R2       =     0.1859

                                   (Std. Err. adjusted for 2258 clusters in leaderid)
-------------------------------------------------------------------------------------
                    |               Robust
             cwinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
       milnoncombat |   .7086517   .1923578     3.68   0.000     .3316373    1.085666
             combat |   .4141407   .1701076     2.43   0.015     .0807359    .7475454
              rebel |   .0221961   .1299026     0.17   0.864    -.2324082    .2768005
             warwin |   .3218007   .1647528     1.95   0.051    -.0011088    .6447103
            warloss |   .0969283   .1696773     0.57   0.568    -.2356331    .4294898
           rebelwin |  -.0605395   .1510243    -0.40   0.689    -.3565416    .2354626
          rebelloss |   .7141976    .265052     2.69   0.007     .1947052     1.23369
           leveledu |   .0462606   .0663042     0.70   0.485    -.0836932    .1762144
                age |   .0102839   .0042992     2.39   0.017     .0018576    .0187102
            teacher |  -.0202248   .1296081    -0.16   0.876    -.2742519    .2338024
         journalism |  -.1359718   .1981908    -0.69   0.493    -.5244188    .2524751
                law |  -.1464982   .1310189    -1.12   0.264    -.4032904    .1102941
           medicine |  -.5529008   .2598877    -2.13   0.033    -1.062271   -.0435302
           religion |   .3620216   .4594624     0.79   0.431    -.5385081    1.262551
           activist |   .1473004   .1300168     1.13   0.257    -.1075279    .4021287
   careerpolitician |   -.057577   .1027884    -0.56   0.575    -.2590386    .1438846
           creative |    .524738   .2454673     2.14   0.033      .043631    1.005845
           business |  -.1006797   .1429239    -0.70   0.481    -.3808055     .179446
aristocratlandowner |  -.2511304   .2122983    -1.18   0.237    -.6672273    .1649666
             police |   .2307327   .4206547     0.55   0.583    -.5937354    1.055201
     militarycareer |   -.302673   .1919031    -1.58   0.115    -.6787962    .0734502
         scienceeng |   .2175702   .2413567     0.90   0.367    -.2554802    .6906205
         bluecollar |  -.0453714   .2007014    -0.23   0.821    -.4387389    .3479961
             gender |  -.3938837   .2863857    -1.38   0.169    -.9551893    .1674218
       totalspouses |  -.0202293   .0185804    -1.09   0.276    -.0566462    .0161875
            married |   .0396029   .3526166     0.11   0.911    -.6515128    .7307187
     marriedinpower |  -.1747878   .1984453    -0.88   0.378    -.5637335    .2141579
           divorced |   -.016662   .1196519    -0.14   0.889    -.2511755    .2178515
         childtotal |   .0039692   .0054605     0.73   0.467    -.0067332    .0146717
       parstability |   .3783283   .1988239     1.90   0.057    -.0113595    .7680161
            illegit |  -.5236266   .2319828    -2.26   0.024    -.9783045   -.0689486
            royalty |  -.2141826   .2088862    -1.03   0.305    -.6235919    .1952268
       orphanbinary |  -.0978384    .257739    -0.38   0.704    -.6029976    .4073208
   officetenure1000 |   .0243156   .0145865     1.67   0.096    -.0042734    .0529046
  yearssincemidinit |  -.3304432   .0229776   -14.38   0.000    -.3754785    -.285408
                 y2 |   .0108669   .0013562     8.01   0.000     .0082088    .0135249
                 y3 |   -.000099   .0000192    -5.14   0.000    -.0001367   -.0000612
              _cons |  -.7621431   .5220308    -1.46   0.144    -1.785305    .2610184
-------------------------------------------------------------------------------------

. predict leaderrisk if e(sample)
(option pr assumed; Pr(cwinit))
(52 missing values generated)

. 
. logit cwinit cinc dem aut syscon irregular tau_lead fiveyearchallengelag lastwarwin lastwarloss lastwardraw yearssince
> midinit y2 y3, robust cluster(ccode)

Iteration 0:   log pseudolikelihood =  -5505.478  
Iteration 1:   log pseudolikelihood = -4388.0857  
Iteration 2:   log pseudolikelihood = -4239.6206  
Iteration 3:   log pseudolikelihood = -4230.9585  
Iteration 4:   log pseudolikelihood = -4230.8574  
Iteration 5:   log pseudolikelihood = -4230.8571  

Logistic regression                               Number of obs   =      11388
                                                  Wald chi2(13)   =     785.87
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -4230.8571                 Pseudo R2       =     0.2315

                                        (Std. Err. adjusted for 178 clusters in ccode)
--------------------------------------------------------------------------------------
                     |               Robust
              cwinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
                cinc |   9.404467   2.027024     4.64   0.000     5.431572    13.37736
                 dem |  -.2396804   .1588788    -1.51   0.131     -.551077    .0717163
                 aut |   .1679045   .1299729     1.29   0.196    -.0868377    .4226466
              syscon |  -3.486719   1.066383    -3.27   0.001    -5.576791   -1.396647
           irregular |   .2121851   .1162016     1.83   0.068    -.0155659    .4399361
            tau_lead |  -.0436213   .1617221    -0.27   0.787    -.3605907    .2733482
fiveyearchallengelag |   .6592767   .0783817     8.41   0.000     .5056514     .812902
          lastwarwin |   .7341308   .1532903     4.79   0.000     .4336873    1.034574
         lastwarloss |   .6475341   .1319239     4.91   0.000      .388968    .9061001
         lastwardraw |    .929418    .211505     4.39   0.000     .5148757     1.34396
   yearssincemidinit |  -.2690812   .0237309   -11.34   0.000    -.3155929   -.2225696
                  y2 |   .0092909    .001325     7.01   0.000     .0066941    .0118878
                  y3 |  -.0000889    .000018    -4.94   0.000    -.0001242   -.0000536
               _cons |  -.7252537   .3385474    -2.14   0.032    -1.388794   -.0617129
--------------------------------------------------------------------------------------

. predict systemrisk if e(sample)
(option pr assumed; Pr(cwinit))
(182 missing values generated)

. 
. collapse leaderrisk systemrisk, by(year ccode)

. 
. describe

Contains data
  obs:        10,677                          Why Leaders Fight - Monadic Replication
 vars:             4                          
 size:       128,124                          (_dta has notes)
------------------------------------------------------------------------------------------------------------------------
              storage   display    value
variable name   type    format     label      variable label
------------------------------------------------------------------------------------------------------------------------
ccode           int     %8.0g                 COW Country Code
year            int     %8.0g                 Year
leaderrisk      float   %9.0g                 (mean) leaderrisk
systemrisk      float   %9.0g                 (mean) systemrisk
------------------------------------------------------------------------------------------------------------------------
Sorted by:  year  ccode
     Note:  dataset has changed since last saved

. summarize

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       ccode |     10677    402.2687    251.0326          2        950
        year |     10677    1957.351    33.92166       1875       2001
  leaderrisk |     10627    .1701697    .1518484   .0000878   .7813953
  systemrisk |     10508    .1645717    .1687123   .0000539   .9103281

. 
. sort ccode year

. tsset ccode year
       panel variable:  ccode (unbalanced)
        time variable:  year, 1875 to 2001, but with gaps
                delta:  1 unit

. 
. */ Standardize variables */
. 
. egen z2systemrisk = std(systemrisk)
(169 missing values generated)

. egen z2leaderrisk = std(leaderrisk)
(50 missing values generated)

. 
. quietly twoway line systemrisk year if ccode==2 || qfit systemrisk year if ccode==2, legend(label(1 "System Risk Score
> ") label(2 "Quadratic Fit") r(2) m(tiny) bm(tiny) si(small)) xlabel(#10) saving(system, replace)

. quietly twoway line leaderrisk year if ccode==2 || qfit leaderrisk year if ccode==2, legend(label(1 "Leader Risk Score
> ") label(2 "Quadratic Fit") r(2) m(tiny) bm(tiny) si(small)) xlabel(#10) saving(leader, replace)

. quietly twoway line z2systemrisk z2leaderrisk year if ccode==2, lp(dash) legend(label(1 "System Risk Score") label(2 "
> Leader Risk Score") r(2) m(tiny) bm(tiny) si(small)) xlabel(#10) saving(zstandardsystemleader, replace)

. 
. */ With quadratic fit lines */
. 
. twoway line z2systemrisk z2leaderrisk year if ccode==2 || qfit z2systemrisk year if ccode==2, lp(dash) || qfit z2leade
> rrisk year if ccode==2, lp(dot) ||, legend(label(1 "System Risk Score") label(2 "Leader Risk Score") label(3 "System R
> isk (Fitted)") label(4 "Leader Risk (Fitted)") r(2) m(tiny) bm(tiny) si(small)) xlabel(#10) ytitle("Risk Score (Standa
> rdized)")

. 
. graph save Graph "ReplicationFigure3_6.gph", replace
(file ReplicationFigure3_6.gph saved)

. 
. clear

. 
. */ Figures 3.8, 3.10, 3.12, and 3.13 */
. 
. use WhyLeadersFightMonadicReplication.dta
(Why Leaders Fight - Monadic Replication)

. 
. logit cwinit milnoncombat combat rebel warwin warloss rebelwin rebelloss leveledu age teacher journalism law medicine 
> religion activist careerpolitician creative business aristocratlandowner police militarycareer scienceeng bluecollar g
> ender totalspouses married marriedinpower divorced childtotal parstability illegit royalty orphanbinary officetenure10
> 00 yearssincemidinit y2 y3, robust cluster(leaderid)

Iteration 0:   log pseudolikelihood = -5579.3482  
Iteration 1:   log pseudolikelihood = -4655.1691  
Iteration 2:   log pseudolikelihood = -4544.2096  
Iteration 3:   log pseudolikelihood = -4542.1008  
Iteration 4:   log pseudolikelihood = -4542.0842  
Iteration 5:   log pseudolikelihood = -4542.0842  

Logistic regression                               Number of obs   =      11518
                                                  Wald chi2(37)   =     591.29
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -4542.0842                 Pseudo R2       =     0.1859

                                   (Std. Err. adjusted for 2258 clusters in leaderid)
-------------------------------------------------------------------------------------
                    |               Robust
             cwinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
       milnoncombat |   .7086517   .1923578     3.68   0.000     .3316373    1.085666
             combat |   .4141407   .1701076     2.43   0.015     .0807359    .7475454
              rebel |   .0221961   .1299026     0.17   0.864    -.2324082    .2768005
             warwin |   .3218007   .1647528     1.95   0.051    -.0011088    .6447103
            warloss |   .0969283   .1696773     0.57   0.568    -.2356331    .4294898
           rebelwin |  -.0605395   .1510243    -0.40   0.689    -.3565416    .2354626
          rebelloss |   .7141976    .265052     2.69   0.007     .1947052     1.23369
           leveledu |   .0462606   .0663042     0.70   0.485    -.0836932    .1762144
                age |   .0102839   .0042992     2.39   0.017     .0018576    .0187102
            teacher |  -.0202248   .1296081    -0.16   0.876    -.2742519    .2338024
         journalism |  -.1359718   .1981908    -0.69   0.493    -.5244188    .2524751
                law |  -.1464982   .1310189    -1.12   0.264    -.4032904    .1102941
           medicine |  -.5529008   .2598877    -2.13   0.033    -1.062271   -.0435302
           religion |   .3620216   .4594624     0.79   0.431    -.5385081    1.262551
           activist |   .1473004   .1300168     1.13   0.257    -.1075279    .4021287
   careerpolitician |   -.057577   .1027884    -0.56   0.575    -.2590386    .1438846
           creative |    .524738   .2454673     2.14   0.033      .043631    1.005845
           business |  -.1006797   .1429239    -0.70   0.481    -.3808055     .179446
aristocratlandowner |  -.2511304   .2122983    -1.18   0.237    -.6672273    .1649666
             police |   .2307327   .4206547     0.55   0.583    -.5937354    1.055201
     militarycareer |   -.302673   .1919031    -1.58   0.115    -.6787962    .0734502
         scienceeng |   .2175702   .2413567     0.90   0.367    -.2554802    .6906205
         bluecollar |  -.0453714   .2007014    -0.23   0.821    -.4387389    .3479961
             gender |  -.3938837   .2863857    -1.38   0.169    -.9551893    .1674218
       totalspouses |  -.0202293   .0185804    -1.09   0.276    -.0566462    .0161875
            married |   .0396029   .3526166     0.11   0.911    -.6515128    .7307187
     marriedinpower |  -.1747878   .1984453    -0.88   0.378    -.5637335    .2141579
           divorced |   -.016662   .1196519    -0.14   0.889    -.2511755    .2178515
         childtotal |   .0039692   .0054605     0.73   0.467    -.0067332    .0146717
       parstability |   .3783283   .1988239     1.90   0.057    -.0113595    .7680161
            illegit |  -.5236266   .2319828    -2.26   0.024    -.9783045   -.0689486
            royalty |  -.2141826   .2088862    -1.03   0.305    -.6235919    .1952268
       orphanbinary |  -.0978384    .257739    -0.38   0.704    -.6029976    .4073208
   officetenure1000 |   .0243156   .0145865     1.67   0.096    -.0042734    .0529046
  yearssincemidinit |  -.3304432   .0229776   -14.38   0.000    -.3754785    -.285408
                 y2 |   .0108669   .0013562     8.01   0.000     .0082088    .0135249
                 y3 |   -.000099   .0000192    -5.14   0.000    -.0001367   -.0000612
              _cons |  -.7621431   .5220308    -1.46   0.144    -1.785305    .2610184
-------------------------------------------------------------------------------------

. predict leaderrisk if e(sample)
(option pr assumed; Pr(cwinit))
(52 missing values generated)

. 
. */ Generate yearly average for each country */
. 
. collapse (mean) leaderrisk, by(year ccode)

. 
. */ Generate yearly average by region */
. 
. gen region=1 if ccode>=0

. replace region=2 if ccode>20
(10468 real changes made)

. replace region=3 if ccode>100
(9120 real changes made)

. replace region=4 if ccode>200
(7796 real changes made)

. replace region=5 if ccode>400
(4804 real changes made)

. replace region=6 if ccode>600
(2921 real changes made)

. replace region=7 if ccode>700
(1700 real changes made)

. 
. collapse (mean) leaderrisk, by(year region)

. 
. mean leaderrisk, over(region)

Mean estimation                     Number of obs    =     889

            1: region = 1
            2: region = 2
            3: region = 3
            4: region = 4
            5: region = 5
            6: region = 6
            7: region = 7

--------------------------------------------------------------
        Over |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
leaderrisk   |
           1 |   .3086492   .0113064      .2864588    .3308396
           2 |   .1376416   .0049301      .1279656    .1473177
           3 |   .1730439   .0041311      .1649359    .1811518
           4 |    .159601   .0041013      .1515517    .1676503
           5 |   .1064231   .0064754      .0937142     .119132
           6 |   .1671949     .00736      .1527498      .18164
           7 |   .1885662   .0058704      .1770448    .2000876
--------------------------------------------------------------

. 
. sort leaderrisk

. sort year

. 
. twoway line leaderrisk year if year>=1900 & region==6 || qfit leaderrisk year if year>=1900 & region==6, lp(dash) lege
> nd(label(1 "Mean Risk Score") label(2 "Quadratic Fit")) xlabel(#10) xlabel(#10)

. graph save Graph "ReplicationFigure3_8.gph", replace
(file ReplicationFigure3_8.gph saved)

. 
. twoway line leaderrisk year if year>=1950 & region==5 || qfit leaderrisk year if year>=1950 & region==5, lp(dash) lege
> nd(label(1 "Mean Risk Score") label(2 "Quadratic Fit")) xlabel(#10) xlabel(#10)

. graph save Graph "ReplicationFigure3_10.gph", replace
(file ReplicationFigure3_10.gph saved)

. 
. twoway line leaderrisk year if region==3 || qfit leaderrisk year if region==3, lp(dash) legend(label(1 "Mean Risk Scor
> e") label(2 "Quadratic Fit")) xlabel(#10) xlabel(#10)

. graph save Graph "ReplicationFigure3_12.gph", replace
(file ReplicationFigure3_12.gph saved)

. 
. twoway line leaderrisk year if region==2 || qfit leaderrisk year if region==2, lp(dash) legend(label(1 "Mean Risk Scor
> e") label(2 "Quadratic Fit")) xlabel(#10) xlabel(#10)

. graph save Graph "ReplicationFigure3_13.gph", replace
(file ReplicationFigure3_13.gph saved)

. 
. estimates clear

. clear

. 
. */ CHAPTER 4 */
. 
. */ Data for Figures 4.1 and 4.2 */
. 
. use WhyLeadersFightMonadicReplication.dta
(Why Leaders Fight - Monadic Replication)

. 
. */ Generate Figure 4.1 data */
. estsimp logit cwinit milnoncombat combat rebel warwin warloss rebelwin rebelloss age aut cinc tau_lead officetenure100
> 0 fiveyearchallengelag yearssincemidinit y2 y3, robust cluster(leaderid)

Iteration 0:   log pseudolikelihood = -5495.0412
Iteration 1:   log pseudolikelihood =  -4409.054
Iteration 2:   log pseudolikelihood = -4272.0683
Iteration 3:   log pseudolikelihood = -4264.9773
Iteration 4:   log pseudolikelihood = -4264.8748
Iteration 5:   log pseudolikelihood = -4264.8743

Logistic regression                               Number of obs   =      11345
                                                  Wald chi2(16)   =     788.89
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -4264.8743                 Pseudo R2       =     0.2239

                            (Std. Err. adjusted for 2236 clusters in leaderid)
------------------------------------------------------------------------------
             |               Robust
      cwinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
milnoncombat |   .3737291   .1348027     2.77   0.006     .1095206    .6379376
      combat |    .102774   .1441593     0.71   0.476     -.179773     .385321
       rebel |   .3854133    .144919     2.66   0.008     .1013772    .6694493
      warwin |   .0698017   .1542228     0.45   0.651    -.2324695    .3720729
     warloss |    .128253   .1474028     0.87   0.384    -.1606513    .4171572
    rebelwin |  -.2478988   .1511329    -1.64   0.101    -.5441137    .0483162
   rebelloss |   .2184371   .2223092     0.98   0.326     -.217281    .6541552
         age |   .0091931   .0054613     1.68   0.092    -.0015109    .0198971
         aut |   .1555457   .1014231     1.53   0.125    -.0432398    .3543313
        cinc |   9.588666   1.335675     7.18   0.000     6.970791    12.20654
    tau_lead |   .1500294   .1215916     1.23   0.217    -.0882857    .3883445
officet~1000 |   .0117652   .0131111     0.90   0.370    -.0139321    .0374625
fiveyearch~g |   .7834416   .0746275    10.50   0.000     .6371744    .9297089
yearssince~t |  -.2795388   .0223704   -12.50   0.000     -.323384   -.2356937
          y2 |   .0096231   .0012694     7.58   0.000     .0071352     .012111
          y3 |  -.0000912    .000018    -5.07   0.000    -.0001265    -.000056
       _cons |   -2.06681   .2823447    -7.32   0.000    -2.620196   -1.513425
------------------------------------------------------------------------------

Simulating main parameters.  Please wait....
% of simulations completed: 5% 11% 17% 23% 29% 35% 41% 47% 52% 58% 64% 70% 76% 82% 88% 94% 100% 

Number of simulations  : 1000
Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9 b10 b11 b12 b13 b14 b15 b16 b17

. setx mean

. simqi, pr

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
              Pr(cwinit=0) |   .8750043     .0042832     .8665806    .8837672
              Pr(cwinit=1) |   .1249957     .0042832     .1162328    .1334194

. setx combat 0 milnoncombat 0 warwin 0 warloss 0

. simqi, pr

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
              Pr(cwinit=0) |   .8840202     .0059404     .8721651    .8951663
              Pr(cwinit=1) |   .1159798     .0059404     .1048337    .1278348

. setx combat 1 warwin mean warloss mean

. simqi, pr

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
              Pr(cwinit=0) |   .8704982     .0121779     .8453233    .8927607
              Pr(cwinit=1) |   .1295018     .0121779     .1072393    .1546767

. setx milnoncombat 1 combat 0 warwin 0 warloss 0

. simqi, pr

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
              Pr(cwinit=0) |   .8394113     .0158701     .8078885    .8681005
              Pr(cwinit=1) |   .1605887     .0158701     .1318995    .1921115

. setx  mean

. setx rebel 0 rebelloss 0 rebelwin 0

. simqi, pr

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
              Pr(cwinit=0) |   .8872323     .0057007     .8757637     .898141
              Pr(cwinit=1) |   .1127677     .0057007      .101859    .1242363

. setx rebel 1 rebelloss mean rebelwin mean

. simqi, pr

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
              Pr(cwinit=0) |   .8440513     .0131523     .8174875    .8681139
              Pr(cwinit=1) |   .1559487     .0131523     .1318861    .1825125

. setx milnoncombat 1 warwin 0 warloss 0

. simqi, pr

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
              Pr(cwinit=0) |    .797435     .0224017     .7512452    .8363416
              Pr(cwinit=1) |    .202565     .0224017     .1636585    .2487548

. 
. */ Graph created using Excel */
. 
. drop b1-b17

. 
. */ Generate Figure 4.2 data */
. 
. gen milaut=milnoncombat*aut
(29 missing values generated)

. label var milaut "Military Service, No Combat * Autocracy"

. gen combataut=combat*aut
(27 missing values generated)

. label var combataut "Military Service, Combat * Autocracy"

. gen rebelaut=rebel*aut
(26 missing values generated)

. label var rebelaut "Prior Rebel Participation * Autocracy"

. gen milrebel=milnoncombat*rebel
(34 missing values generated)

. label var milrebel "Military Service, No Combat * Rebel"

. gen combatrebel=combat*rebel
(36 missing values generated)

. label var combatrebel "Military Service, Combat * Rebel"

. 
. estsimp logit cwinit milnoncombat combat rebel milaut combataut rebelaut milrebel combatrebel warwin warloss rebelwin 
> rebelloss age aut cinc tau_lead officetenure1000 fiveyearchallengelag yearssincemidinit y2 y3, robust cluster(leaderid
> )

Iteration 0:   log pseudolikelihood = -5495.0412
Iteration 1:   log pseudolikelihood = -4393.9231
Iteration 2:   log pseudolikelihood = -4255.6177
Iteration 3:   log pseudolikelihood = -4248.5502
Iteration 4:   log pseudolikelihood = -4248.4451
Iteration 5:   log pseudolikelihood = -4248.4446

Logistic regression                               Number of obs   =      11345
                                                  Wald chi2(21)   =     814.28
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -4248.4446                 Pseudo R2       =     0.2269

                            (Std. Err. adjusted for 2236 clusters in leaderid)
------------------------------------------------------------------------------
             |               Robust
      cwinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
milnoncombat |      .1738   .1658019     1.05   0.295    -.1511657    .4987657
      combat |   .1055285   .1924495     0.55   0.583    -.2716657    .4827226
       rebel |   .5939523   .2258973     2.63   0.009     .1512018    1.036703
      milaut |   .8189574   .2376177     3.45   0.001     .3532353     1.28468
   combataut |   .6206236   .2168624     2.86   0.004     .1955812    1.045666
    rebelaut |  -.1736009   .1996855    -0.87   0.385    -.5649773    .2177755
    milrebel |  -.3354042   .2871358    -1.17   0.243    -.8981801    .2273718
 combatrebel |  -.3683422   .2352742    -1.57   0.117    -.8294713    .0927868
      warwin |   .0290636   .1596151     0.18   0.856    -.2837762    .3419035
     warloss |   .1008095   .1416484     0.71   0.477    -.1768163    .3784352
    rebelwin |  -.1933003   .1449749    -1.33   0.182    -.4774458    .0908453
   rebelloss |   .2689925   .2252639     1.19   0.232    -.1725167    .7105017
         age |   .0080954   .0048991     1.65   0.098    -.0015066    .0176973
         aut |  -.1224737   .1525844    -0.80   0.422    -.4215336    .1765863
        cinc |   9.466919    1.30078     7.28   0.000     6.917437     12.0164
    tau_lead |   .1629658   .1194783     1.36   0.173    -.0712074    .3971389
officet~1000 |   .0174058   .0135037     1.29   0.197    -.0090609    .0438725
fiveyearch~g |   .7871646   .0743806    10.58   0.000     .6413813     .932948
yearssince~t |  -.2778575   .0220176   -12.62   0.000    -.3210112   -.2347037
          y2 |   .0095918    .001262     7.60   0.000     .0071184    .0120652
          y3 |  -.0000912    .000018    -5.08   0.000    -.0001264    -.000056
       _cons |  -2.001148   .2741859    -7.30   0.000    -2.538543   -1.463754
------------------------------------------------------------------------------

Simulating main parameters.  Please wait....
% of simulations completed: 4% 9% 13% 18% 22% 27% 31% 36% 40% 45% 50% 54% 59% 63% 68% 72% 77% 81% 86% 90% 95% 100% 

Number of simulations  : 1000
Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9 b10 b11 b12 b13 b14 b15 b16 b17 b18 b19 b20 b21 b22

. setx mean

. setx milnoncombat 0 combat 0 rebel 0 milrebel 0 combatrebel 0 warwin 0 warloss 0 rebelwin 0 rebelloss 0 aut 0 milaut 0
>  combataut 0 rebelaut 0 age mean cinc mean tau_lead mean officetenure1000 mean yearssincemidinit mean y2 mean y3 mean

. simqi, pr

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
              Pr(cwinit=0) |   .8967507     .0069786     .8829313    .9099176
              Pr(cwinit=1) |   .1032493     .0069786     .0900824    .1170687

. setx milnoncombat 0 combat 1 rebel 0 milrebel 0 combatrebel 0 warwin 0 warloss 0 rebelwin 0 rebelloss 0 aut 0 milaut 0
>  combataut 0 rebelaut 0 age mean cinc mean tau_lead mean officetenure1000 mean yearssincemidinit mean y2 mean y3 mean

. simqi, pr

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
              Pr(cwinit=0) |   .8858181     .0180947     .8487815     .919092
              Pr(cwinit=1) |   .1141819     .0180947      .080908    .1512185

. setx milnoncombat 0 combat 1 rebel 0 milrebel 0 combatrebel 0 warwin 0 warloss 0 rebelwin 0 rebelloss 0 aut 1 milaut 0
>  combataut 1 rebelaut 0 age mean cinc mean tau_lead mean officetenure1000 mean yearssincemidinit mean y2 mean y3 mean

. simqi, pr

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
              Pr(cwinit=0) |   .8245779     .0319694     .7572721    .8791264
              Pr(cwinit=1) |   .1754221     .0319694     .1208736     .242728

. setx milnoncombat 0 combat 0 rebel 0 milrebel 0 combatrebel 0 warwin 0 warloss 0 rebelwin 0 rebelloss 0 aut 1 milaut 0
>  combataut 0 rebelaut 0 age mean cinc mean tau_lead mean officetenure1000 mean yearssincemidinit mean y2 mean y3 mean

. simqi, pr

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
              Pr(cwinit=0) |   .9073755     .0115226     .8830481    .9285703
              Pr(cwinit=1) |   .0926245     .0115226     .0714297    .1169519

. 
. */ Graph created using Excel */
. 
. drop b1-b22

. 
. */ Start generating regression models for table A.8 in technical appendix */
. logit cwinit milnoncombat combat rebel warwin warloss rebelwin rebelloss age aut cinc tau_lead officetenure1000 fiveye
> archallengelag yearssincemidinit y2 y3, robust cluster(leaderid)

Iteration 0:   log pseudolikelihood = -5495.0412  
Iteration 1:   log pseudolikelihood =  -4409.054  
Iteration 2:   log pseudolikelihood = -4272.0683  
Iteration 3:   log pseudolikelihood = -4264.9395  
Iteration 4:   log pseudolikelihood = -4264.8744  
Iteration 5:   log pseudolikelihood = -4264.8743  

Logistic regression                               Number of obs   =      11345
                                                  Wald chi2(16)   =     788.89
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -4264.8743                 Pseudo R2       =     0.2239

                                    (Std. Err. adjusted for 2236 clusters in leaderid)
--------------------------------------------------------------------------------------
                     |               Robust
              cwinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
        milnoncombat |   .3737291   .1348028     2.77   0.006     .1095205    .6379376
              combat |    .102774   .1441593     0.71   0.476    -.1797731    .3853211
               rebel |   .3854133   .1449191     2.66   0.008     .1013771    .6694495
              warwin |   .0698016   .1542229     0.45   0.651    -.2324697     .372073
             warloss |    .128253   .1474029     0.87   0.384    -.1606514    .4171573
            rebelwin |  -.2478988   .1511329    -1.64   0.101    -.5441139    .0483162
           rebelloss |   .2184371   .2223093     0.98   0.326    -.2172811    .6541553
                 age |   .0091931   .0054613     1.68   0.092    -.0015109    .0198971
                 aut |   .1555457   .1014231     1.53   0.125    -.0432399    .3543313
                cinc |   9.588666   1.335675     7.18   0.000     6.970791    12.20654
            tau_lead |   .1500294   .1215916     1.23   0.217    -.0882858    .3883446
    officetenure1000 |   .0117652   .0131111     0.90   0.370    -.0139321    .0374625
fiveyearchallengelag |   .7834417   .0746276    10.50   0.000     .6371743     .929709
   yearssincemidinit |  -.2795386   .0223707   -12.50   0.000    -.3233845   -.2356928
                  y2 |   .0096231   .0012694     7.58   0.000     .0071351     .012111
                  y3 |  -.0000912    .000018    -5.07   0.000    -.0001265    -.000056
               _cons |  -2.066811   .2823448    -7.32   0.000    -2.620196   -1.513425
--------------------------------------------------------------------------------------

. estimates store m1

. 
. logit cwinit milnoncombat combat rebel milaut combataut rebelaut milrebel combatrebel warwin warloss rebelwin rebellos
> s age aut cinc tau_lead officetenure1000 fiveyearchallengelag yearssincemidinit y2 y3, robust cluster(leaderid)

Iteration 0:   log pseudolikelihood = -5495.0412  
Iteration 1:   log pseudolikelihood = -4393.9231  
Iteration 2:   log pseudolikelihood = -4255.6177  
Iteration 3:   log pseudolikelihood = -4248.5095  
Iteration 4:   log pseudolikelihood = -4248.4447  
Iteration 5:   log pseudolikelihood = -4248.4446  

Logistic regression                               Number of obs   =      11345
                                                  Wald chi2(21)   =     814.28
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -4248.4446                 Pseudo R2       =     0.2269

                                    (Std. Err. adjusted for 2236 clusters in leaderid)
--------------------------------------------------------------------------------------
                     |               Robust
              cwinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
        milnoncombat |      .1738   .1658019     1.05   0.295    -.1511658    .4987658
              combat |   .1055285   .1924496     0.55   0.583    -.2716659    .4827228
               rebel |   .5939523   .2258974     2.63   0.009     .1512016    1.036703
              milaut |   .8189574   .2376178     3.45   0.001     .3532351     1.28468
           combataut |   .6206236   .2168624     2.86   0.004     .1955811    1.045666
            rebelaut |  -.1736009   .1996856    -0.87   0.385    -.5649774    .2177757
            milrebel |  -.3354041   .2871359    -1.17   0.243    -.8981802     .227372
         combatrebel |  -.3683422   .2352744    -1.57   0.117    -.8294715     .092787
              warwin |   .0290636   .1596152     0.18   0.856    -.2837764    .3419036
             warloss |   .1008095   .1416484     0.71   0.477    -.1768164    .3784353
            rebelwin |  -.1933003   .1449749    -1.33   0.182    -.4774459    .0908453
           rebelloss |   .2689925    .225264     1.19   0.232    -.1725168    .7105018
                 age |   .0080954   .0048991     1.65   0.098    -.0015066    .0176974
                 aut |  -.1224737   .1525845    -0.80   0.422    -.4215337    .1765863
                cinc |    9.46692    1.30078     7.28   0.000     6.917437     12.0164
            tau_lead |   .1629658   .1194783     1.36   0.173    -.0712075     .397139
    officetenure1000 |   .0174058   .0135037     1.29   0.197    -.0090609    .0438725
fiveyearchallengelag |   .7871646   .0743807    10.58   0.000     .6413812    .9329481
   yearssincemidinit |  -.2778573    .022018   -12.62   0.000    -.3210117   -.2347028
                  y2 |   .0095918    .001262     7.60   0.000     .0071183    .0120652
                  y3 |  -.0000912    .000018    -5.08   0.000    -.0001264    -.000056
               _cons |  -2.001149    .274186    -7.30   0.000    -2.538543   -1.463754
--------------------------------------------------------------------------------------

. estimates store m2

. 
. */ Figure 4.3 */
. 
. logit cwinit milnoncombat combat rebel warwin warloss rebelwin rebelloss leveledu age teacher journalism law medicine 
> religion activist careerpolitician creative business aristocratlandowner police militarycareer interpreter scienceeng 
> gender married marriedinpower divorced totalspouses childtotal parstability illegit royalty orphanbinary officetenure1
> 000 yearssincemidinit y2 y3, robust cluster(leaderid)

Iteration 0:   log pseudolikelihood = -5579.3482  
Iteration 1:   log pseudolikelihood = -4653.9686  
Iteration 2:   log pseudolikelihood =  -4542.909  
Iteration 3:   log pseudolikelihood = -4540.8098  
Iteration 4:   log pseudolikelihood = -4540.7935  
Iteration 5:   log pseudolikelihood = -4540.7935  

Logistic regression                               Number of obs   =      11518
                                                  Wald chi2(37)   =     587.27
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -4540.7935                 Pseudo R2       =     0.1861

                                   (Std. Err. adjusted for 2258 clusters in leaderid)
-------------------------------------------------------------------------------------
                    |               Robust
             cwinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
       milnoncombat |   .6988157   .1893264     3.69   0.000     .3277427    1.069889
             combat |   .3976785    .165228     2.41   0.016     .0738376    .7215193
              rebel |   .0286267   .1314537     0.22   0.828    -.2290178    .2862713
             warwin |   .3233197   .1657861     1.95   0.051    -.0016151    .6482544
            warloss |   .1012968   .1708099     0.59   0.553    -.2334844     .436078
           rebelwin |  -.0619912   .1518032    -0.41   0.683      -.35952    .2355375
          rebelloss |   .7089002   .2669738     2.66   0.008     .1856412    1.232159
           leveledu |   .0450728   .0650854     0.69   0.489    -.0824922    .1726378
                age |   .0101947   .0043365     2.35   0.019     .0016952    .0186941
            teacher |  -.0192173   .1309835    -0.15   0.883    -.2759403    .2375057
         journalism |  -.1367645   .1987313    -0.69   0.491    -.5262707    .2527418
                law |  -.1367669    .132513    -1.03   0.302    -.3964876    .1229537
           medicine |  -.5574292   .2601279    -2.14   0.032    -1.067271   -.0475878
           religion |   .3663464   .4597185     0.80   0.426    -.5346853    1.267378
           activist |    .146511   .1279836     1.14   0.252    -.1043321    .3973542
   careerpolitician |  -.0547404   .1017393    -0.54   0.591    -.2541458    .1446651
           creative |   .5259843   .2458006     2.14   0.032     .0442239    1.007745
           business |  -.0996908   .1444048    -0.69   0.490     -.382719    .1833375
aristocratlandowner |  -.2615203    .213253    -1.23   0.220    -.6794884    .1564479
             police |   .2310804   .4184595     0.55   0.581    -.5890852    1.051246
     militarycareer |  -.2918658   .1823634    -1.60   0.109    -.6492916      .06556
        interpreter |  -.9258599   .3520092    -2.63   0.009    -1.615785   -.2359346
         scienceeng |   .2165745   .2398095     0.90   0.366    -.2534434    .6865924
             gender |  -.3939817   .2860136    -1.38   0.168     -.954558    .1665946
            married |   .0402884   .3525392     0.11   0.909    -.6506757    .7312524
     marriedinpower |   -.173667   .1996851    -0.87   0.384    -.5650427    .2177087
           divorced |  -.0138731   .1182144    -0.12   0.907    -.2455691    .2178229
       totalspouses |  -.0209273   .0187624    -1.12   0.265    -.0577009    .0158463
         childtotal |   .0037589    .005488     0.68   0.493    -.0069974    .0145151
       parstability |   .3780889   .1992589     1.90   0.058    -.0124513    .7686291
            illegit |  -.4778056   .2273774    -2.10   0.036    -.9234572    -.032154
            royalty |   -.189686   .2098176    -0.90   0.366     -.600921     .221549
       orphanbinary |  -.1159915   .2549451    -0.45   0.649    -.6156748    .3836917
   officetenure1000 |   .0237979   .0146128     1.63   0.103    -.0048428    .0524385
  yearssincemidinit |  -.3304183   .0229231   -14.41   0.000    -.3753468   -.2854898
                 y2 |   .0108567   .0013536     8.02   0.000     .0082037    .0135097
                 y3 |  -.0000988   .0000192    -5.15   0.000    -.0001364   -.0000612
              _cons |  -.7611295   .5193116    -1.47   0.143    -1.778962    .2567026
-------------------------------------------------------------------------------------

. estimates store m3

. 
. margins, at(age=(20(5)100)) atmeans vsquish post

Adjusted predictions                              Number of obs   =      11518
Model VCE    : Robust

Expression   : Pr(cwinit), predict()
1._at        : milnoncombat    =    .1074839 (mean)
               combat          =    .2647161 (mean)
               rebel           =    .3433756 (mean)
               warwin          =    .0973259 (mean)
               warloss         =    .0869074 (mean)
               rebelwin        =     .089078 (mean)
               rebelloss       =    .0288244 (mean)
               leveledu        =    2.059472 (mean)
               age             =          20
               teacher         =    .1338774 (mean)
               journalism      =    .0586907 (mean)
               law             =    .2217399 (mean)
               medicine        =    .0330787 (mean)
               religion        =    .0230943 (mean)
               activist        =    .1774614 (mean)
               careerpoli~n    =    .3346067 (mean)
               creative        =    .0633791 (mean)
               business        =    .0965445 (mean)
               aristocrat~r    =    .1131273 (mean)
               police          =     .009203 (mean)
               militaryca~r    =    .2426637 (mean)
               interpreter     =    .0037333 (mean)
               scienceeng      =    .0408925 (mean)
               gender          =    .9853273 (mean)
               married         =    .9576315 (mean)
               marriedinp~r    =    .9186491 (mean)
               divorced        =    .1479424 (mean)
               totalspouses    =    1.238062 (mean)
               childtotal      =    4.014412 (mean)
               parstability    =    .0711929 (mean)
               illegit         =    .0322105 (mean)
               royalty         =    .1449036 (mean)
               orphanbinary    =    .0331655 (mean)
               officet~1000    =    2.274386 (mean)
               yearssince~t    =    10.27783 (mean)
               y2              =    274.4617 (mean)
               y3              =    10610.92 (mean)
2._at        : milnoncombat    =    .1074839 (mean)
               combat          =    .2647161 (mean)
               rebel           =    .3433756 (mean)
               warwin          =    .0973259 (mean)
               warloss         =    .0869074 (mean)
               rebelwin        =     .089078 (mean)
               rebelloss       =    .0288244 (mean)
               leveledu        =    2.059472 (mean)
               age             =          25
               teacher         =    .1338774 (mean)
               journalism      =    .0586907 (mean)
               law             =    .2217399 (mean)
               medicine        =    .0330787 (mean)
               religion        =    .0230943 (mean)
               activist        =    .1774614 (mean)
               careerpoli~n    =    .3346067 (mean)
               creative        =    .0633791 (mean)
               business        =    .0965445 (mean)
               aristocrat~r    =    .1131273 (mean)
               police          =     .009203 (mean)
               militaryca~r    =    .2426637 (mean)
               interpreter     =    .0037333 (mean)
               scienceeng      =    .0408925 (mean)
               gender          =    .9853273 (mean)
               married         =    .9576315 (mean)
               marriedinp~r    =    .9186491 (mean)
               divorced        =    .1479424 (mean)
               totalspouses    =    1.238062 (mean)
               childtotal      =    4.014412 (mean)
               parstability    =    .0711929 (mean)
               illegit         =    .0322105 (mean)
               royalty         =    .1449036 (mean)
               orphanbinary    =    .0331655 (mean)
               officet~1000    =    2.274386 (mean)
               yearssince~t    =    10.27783 (mean)
               y2              =    274.4617 (mean)
               y3              =    10610.92 (mean)
3._at        : milnoncombat    =    .1074839 (mean)
               combat          =    .2647161 (mean)
               rebel           =    .3433756 (mean)
               warwin          =    .0973259 (mean)
               warloss         =    .0869074 (mean)
               rebelwin        =     .089078 (mean)
               rebelloss       =    .0288244 (mean)
               leveledu        =    2.059472 (mean)
               age             =          30
               teacher         =    .1338774 (mean)
               journalism      =    .0586907 (mean)
               law             =    .2217399 (mean)
               medicine        =    .0330787 (mean)
               religion        =    .0230943 (mean)
               activist        =    .1774614 (mean)
               careerpoli~n    =    .3346067 (mean)
               creative        =    .0633791 (mean)
               business        =    .0965445 (mean)
               aristocrat~r    =    .1131273 (mean)
               police          =     .009203 (mean)
               militaryca~r    =    .2426637 (mean)
               interpreter     =    .0037333 (mean)
               scienceeng      =    .0408925 (mean)
               gender          =    .9853273 (mean)
               married         =    .9576315 (mean)
               marriedinp~r    =    .9186491 (mean)
               divorced        =    .1479424 (mean)
               totalspouses    =    1.238062 (mean)
               childtotal      =    4.014412 (mean)
               parstability    =    .0711929 (mean)
               illegit         =    .0322105 (mean)
               royalty         =    .1449036 (mean)
               orphanbinary    =    .0331655 (mean)
               officet~1000    =    2.274386 (mean)
               yearssince~t    =    10.27783 (mean)
               y2              =    274.4617 (mean)
               y3              =    10610.92 (mean)
4._at        : milnoncombat    =    .1074839 (mean)
               combat          =    .2647161 (mean)
               rebel           =    .3433756 (mean)
               warwin          =    .0973259 (mean)
               warloss         =    .0869074 (mean)
               rebelwin        =     .089078 (mean)
               rebelloss       =    .0288244 (mean)
               leveledu        =    2.059472 (mean)
               age             =          35
               teacher         =    .1338774 (mean)
               journalism      =    .0586907 (mean)
               law             =    .2217399 (mean)
               medicine        =    .0330787 (mean)
               religion        =    .0230943 (mean)
               activist        =    .1774614 (mean)
               careerpoli~n    =    .3346067 (mean)
               creative        =    .0633791 (mean)
               business        =    .0965445 (mean)
               aristocrat~r    =    .1131273 (mean)
               police          =     .009203 (mean)
               militaryca~r    =    .2426637 (mean)
               interpreter     =    .0037333 (mean)
               scienceeng      =    .0408925 (mean)
               gender          =    .9853273 (mean)
               married         =    .9576315 (mean)
               marriedinp~r    =    .9186491 (mean)
               divorced        =    .1479424 (mean)
               totalspouses    =    1.238062 (mean)
               childtotal      =    4.014412 (mean)
               parstability    =    .0711929 (mean)
               illegit         =    .0322105 (mean)
               royalty         =    .1449036 (mean)
               orphanbinary    =    .0331655 (mean)
               officet~1000    =    2.274386 (mean)
               yearssince~t    =    10.27783 (mean)
               y2              =    274.4617 (mean)
               y3              =    10610.92 (mean)
5._at        : milnoncombat    =    .1074839 (mean)
               combat          =    .2647161 (mean)
               rebel           =    .3433756 (mean)
               warwin          =    .0973259 (mean)
               warloss         =    .0869074 (mean)
               rebelwin        =     .089078 (mean)
               rebelloss       =    .0288244 (mean)
               leveledu        =    2.059472 (mean)
               age             =          40
               teacher         =    .1338774 (mean)
               journalism      =    .0586907 (mean)
               law             =    .2217399 (mean)
               medicine        =    .0330787 (mean)
               religion        =    .0230943 (mean)
               activist        =    .1774614 (mean)
               careerpoli~n    =    .3346067 (mean)
               creative        =    .0633791 (mean)
               business        =    .0965445 (mean)
               aristocrat~r    =    .1131273 (mean)
               police          =     .009203 (mean)
               militaryca~r    =    .2426637 (mean)
               interpreter     =    .0037333 (mean)
               scienceeng      =    .0408925 (mean)
               gender          =    .9853273 (mean)
               married         =    .9576315 (mean)
               marriedinp~r    =    .9186491 (mean)
               divorced        =    .1479424 (mean)
               totalspouses    =    1.238062 (mean)
               childtotal      =    4.014412 (mean)
               parstability    =    .0711929 (mean)
               illegit         =    .0322105 (mean)
               royalty         =    .1449036 (mean)
               orphanbinary    =    .0331655 (mean)
               officet~1000    =    2.274386 (mean)
               yearssince~t    =    10.27783 (mean)
               y2              =    274.4617 (mean)
               y3              =    10610.92 (mean)
6._at        : milnoncombat    =    .1074839 (mean)
               combat          =    .2647161 (mean)
               rebel           =    .3433756 (mean)
               warwin          =    .0973259 (mean)
               warloss         =    .0869074 (mean)
               rebelwin        =     .089078 (mean)
               rebelloss       =    .0288244 (mean)
               leveledu        =    2.059472 (mean)
               age             =          45
               teacher         =    .1338774 (mean)
               journalism      =    .0586907 (mean)
               law             =    .2217399 (mean)
               medicine        =    .0330787 (mean)
               religion        =    .0230943 (mean)
               activist        =    .1774614 (mean)
               careerpoli~n    =    .3346067 (mean)
               creative        =    .0633791 (mean)
               business        =    .0965445 (mean)
               aristocrat~r    =    .1131273 (mean)
               police          =     .009203 (mean)
               militaryca~r    =    .2426637 (mean)
               interpreter     =    .0037333 (mean)
               scienceeng      =    .0408925 (mean)
               gender          =    .9853273 (mean)
               married         =    .9576315 (mean)
               marriedinp~r    =    .9186491 (mean)
               divorced        =    .1479424 (mean)
               totalspouses    =    1.238062 (mean)
               childtotal      =    4.014412 (mean)
               parstability    =    .0711929 (mean)
               illegit         =    .0322105 (mean)
               royalty         =    .1449036 (mean)
               orphanbinary    =    .0331655 (mean)
               officet~1000    =    2.274386 (mean)
               yearssince~t    =    10.27783 (mean)
               y2              =    274.4617 (mean)
               y3              =    10610.92 (mean)
7._at        : milnoncombat    =    .1074839 (mean)
               combat          =    .2647161 (mean)
               rebel           =    .3433756 (mean)
               warwin          =    .0973259 (mean)
               warloss         =    .0869074 (mean)
               rebelwin        =     .089078 (mean)
               rebelloss       =    .0288244 (mean)
               leveledu        =    2.059472 (mean)
               age             =          50
               teacher         =    .1338774 (mean)
               journalism      =    .0586907 (mean)
               law             =    .2217399 (mean)
               medicine        =    .0330787 (mean)
               religion        =    .0230943 (mean)
               activist        =    .1774614 (mean)
               careerpoli~n    =    .3346067 (mean)
               creative        =    .0633791 (mean)
               business        =    .0965445 (mean)
               aristocrat~r    =    .1131273 (mean)
               police          =     .009203 (mean)
               militaryca~r    =    .2426637 (mean)
               interpreter     =    .0037333 (mean)
               scienceeng      =    .0408925 (mean)
               gender          =    .9853273 (mean)
               married         =    .9576315 (mean)
               marriedinp~r    =    .9186491 (mean)
               divorced        =    .1479424 (mean)
               totalspouses    =    1.238062 (mean)
               childtotal      =    4.014412 (mean)
               parstability    =    .0711929 (mean)
               illegit         =    .0322105 (mean)
               royalty         =    .1449036 (mean)
               orphanbinary    =    .0331655 (mean)
               officet~1000    =    2.274386 (mean)
               yearssince~t    =    10.27783 (mean)
               y2              =    274.4617 (mean)
               y3              =    10610.92 (mean)
8._at        : milnoncombat    =    .1074839 (mean)
               combat          =    .2647161 (mean)
               rebel           =    .3433756 (mean)
               warwin          =    .0973259 (mean)
               warloss         =    .0869074 (mean)
               rebelwin        =     .089078 (mean)
               rebelloss       =    .0288244 (mean)
               leveledu        =    2.059472 (mean)
               age             =          55
               teacher         =    .1338774 (mean)
               journalism      =    .0586907 (mean)
               law             =    .2217399 (mean)
               medicine        =    .0330787 (mean)
               religion        =    .0230943 (mean)
               activist        =    .1774614 (mean)
               careerpoli~n    =    .3346067 (mean)
               creative        =    .0633791 (mean)
               business        =    .0965445 (mean)
               aristocrat~r    =    .1131273 (mean)
               police          =     .009203 (mean)
               militaryca~r    =    .2426637 (mean)
               interpreter     =    .0037333 (mean)
               scienceeng      =    .0408925 (mean)
               gender          =    .9853273 (mean)
               married         =    .9576315 (mean)
               marriedinp~r    =    .9186491 (mean)
               divorced        =    .1479424 (mean)
               totalspouses    =    1.238062 (mean)
               childtotal      =    4.014412 (mean)
               parstability    =    .0711929 (mean)
               illegit         =    .0322105 (mean)
               royalty         =    .1449036 (mean)
               orphanbinary    =    .0331655 (mean)
               officet~1000    =    2.274386 (mean)
               yearssince~t    =    10.27783 (mean)
               y2              =    274.4617 (mean)
               y3              =    10610.92 (mean)
9._at        : milnoncombat    =    .1074839 (mean)
               combat          =    .2647161 (mean)
               rebel           =    .3433756 (mean)
               warwin          =    .0973259 (mean)
               warloss         =    .0869074 (mean)
               rebelwin        =     .089078 (mean)
               rebelloss       =    .0288244 (mean)
               leveledu        =    2.059472 (mean)
               age             =          60
               teacher         =    .1338774 (mean)
               journalism      =    .0586907 (mean)
               law             =    .2217399 (mean)
               medicine        =    .0330787 (mean)
               religion        =    .0230943 (mean)
               activist        =    .1774614 (mean)
               careerpoli~n    =    .3346067 (mean)
               creative        =    .0633791 (mean)
               business        =    .0965445 (mean)
               aristocrat~r    =    .1131273 (mean)
               police          =     .009203 (mean)
               militaryca~r    =    .2426637 (mean)
               interpreter     =    .0037333 (mean)
               scienceeng      =    .0408925 (mean)
               gender          =    .9853273 (mean)
               married         =    .9576315 (mean)
               marriedinp~r    =    .9186491 (mean)
               divorced        =    .1479424 (mean)
               totalspouses    =    1.238062 (mean)
               childtotal      =    4.014412 (mean)
               parstability    =    .0711929 (mean)
               illegit         =    .0322105 (mean)
               royalty         =    .1449036 (mean)
               orphanbinary    =    .0331655 (mean)
               officet~1000    =    2.274386 (mean)
               yearssince~t    =    10.27783 (mean)
               y2              =    274.4617 (mean)
               y3              =    10610.92 (mean)
10._at       : milnoncombat    =    .1074839 (mean)
               combat          =    .2647161 (mean)
               rebel           =    .3433756 (mean)
               warwin          =    .0973259 (mean)
               warloss         =    .0869074 (mean)
               rebelwin        =     .089078 (mean)
               rebelloss       =    .0288244 (mean)
               leveledu        =    2.059472 (mean)
               age             =          65
               teacher         =    .1338774 (mean)
               journalism      =    .0586907 (mean)
               law             =    .2217399 (mean)
               medicine        =    .0330787 (mean)
               religion        =    .0230943 (mean)
               activist        =    .1774614 (mean)
               careerpoli~n    =    .3346067 (mean)
               creative        =    .0633791 (mean)
               business        =    .0965445 (mean)
               aristocrat~r    =    .1131273 (mean)
               police          =     .009203 (mean)
               militaryca~r    =    .2426637 (mean)
               interpreter     =    .0037333 (mean)
               scienceeng      =    .0408925 (mean)
               gender          =    .9853273 (mean)
               married         =    .9576315 (mean)
               marriedinp~r    =    .9186491 (mean)
               divorced        =    .1479424 (mean)
               totalspouses    =    1.238062 (mean)
               childtotal      =    4.014412 (mean)
               parstability    =    .0711929 (mean)
               illegit         =    .0322105 (mean)
               royalty         =    .1449036 (mean)
               orphanbinary    =    .0331655 (mean)
               officet~1000    =    2.274386 (mean)
               yearssince~t    =    10.27783 (mean)
               y2              =    274.4617 (mean)
               y3              =    10610.92 (mean)
11._at       : milnoncombat    =    .1074839 (mean)
               combat          =    .2647161 (mean)
               rebel           =    .3433756 (mean)
               warwin          =    .0973259 (mean)
               warloss         =    .0869074 (mean)
               rebelwin        =     .089078 (mean)
               rebelloss       =    .0288244 (mean)
               leveledu        =    2.059472 (mean)
               age             =          70
               teacher         =    .1338774 (mean)
               journalism      =    .0586907 (mean)
               law             =    .2217399 (mean)
               medicine        =    .0330787 (mean)
               religion        =    .0230943 (mean)
               activist        =    .1774614 (mean)
               careerpoli~n    =    .3346067 (mean)
               creative        =    .0633791 (mean)
               business        =    .0965445 (mean)
               aristocrat~r    =    .1131273 (mean)
               police          =     .009203 (mean)
               militaryca~r    =    .2426637 (mean)
               interpreter     =    .0037333 (mean)
               scienceeng      =    .0408925 (mean)
               gender          =    .9853273 (mean)
               married         =    .9576315 (mean)
               marriedinp~r    =    .9186491 (mean)
               divorced        =    .1479424 (mean)
               totalspouses    =    1.238062 (mean)
               childtotal      =    4.014412 (mean)
               parstability    =    .0711929 (mean)
               illegit         =    .0322105 (mean)
               royalty         =    .1449036 (mean)
               orphanbinary    =    .0331655 (mean)
               officet~1000    =    2.274386 (mean)
               yearssince~t    =    10.27783 (mean)
               y2              =    274.4617 (mean)
               y3              =    10610.92 (mean)
12._at       : milnoncombat    =    .1074839 (mean)
               combat          =    .2647161 (mean)
               rebel           =    .3433756 (mean)
               warwin          =    .0973259 (mean)
               warloss         =    .0869074 (mean)
               rebelwin        =     .089078 (mean)
               rebelloss       =    .0288244 (mean)
               leveledu        =    2.059472 (mean)
               age             =          75
               teacher         =    .1338774 (mean)
               journalism      =    .0586907 (mean)
               law             =    .2217399 (mean)
               medicine        =    .0330787 (mean)
               religion        =    .0230943 (mean)
               activist        =    .1774614 (mean)
               careerpoli~n    =    .3346067 (mean)
               creative        =    .0633791 (mean)
               business        =    .0965445 (mean)
               aristocrat~r    =    .1131273 (mean)
               police          =     .009203 (mean)
               militaryca~r    =    .2426637 (mean)
               interpreter     =    .0037333 (mean)
               scienceeng      =    .0408925 (mean)
               gender          =    .9853273 (mean)
               married         =    .9576315 (mean)
               marriedinp~r    =    .9186491 (mean)
               divorced        =    .1479424 (mean)
               totalspouses    =    1.238062 (mean)
               childtotal      =    4.014412 (mean)
               parstability    =    .0711929 (mean)
               illegit         =    .0322105 (mean)
               royalty         =    .1449036 (mean)
               orphanbinary    =    .0331655 (mean)
               officet~1000    =    2.274386 (mean)
               yearssince~t    =    10.27783 (mean)
               y2              =    274.4617 (mean)
               y3              =    10610.92 (mean)
13._at       : milnoncombat    =    .1074839 (mean)
               combat          =    .2647161 (mean)
               rebel           =    .3433756 (mean)
               warwin          =    .0973259 (mean)
               warloss         =    .0869074 (mean)
               rebelwin        =     .089078 (mean)
               rebelloss       =    .0288244 (mean)
               leveledu        =    2.059472 (mean)
               age             =          80
               teacher         =    .1338774 (mean)
               journalism      =    .0586907 (mean)
               law             =    .2217399 (mean)
               medicine        =    .0330787 (mean)
               religion        =    .0230943 (mean)
               activist        =    .1774614 (mean)
               careerpoli~n    =    .3346067 (mean)
               creative        =    .0633791 (mean)
               business        =    .0965445 (mean)
               aristocrat~r    =    .1131273 (mean)
               police          =     .009203 (mean)
               militaryca~r    =    .2426637 (mean)
               interpreter     =    .0037333 (mean)
               scienceeng      =    .0408925 (mean)
               gender          =    .9853273 (mean)
               married         =    .9576315 (mean)
               marriedinp~r    =    .9186491 (mean)
               divorced        =    .1479424 (mean)
               totalspouses    =    1.238062 (mean)
               childtotal      =    4.014412 (mean)
               parstability    =    .0711929 (mean)
               illegit         =    .0322105 (mean)
               royalty         =    .1449036 (mean)
               orphanbinary    =    .0331655 (mean)
               officet~1000    =    2.274386 (mean)
               yearssince~t    =    10.27783 (mean)
               y2              =    274.4617 (mean)
               y3              =    10610.92 (mean)
14._at       : milnoncombat    =    .1074839 (mean)
               combat          =    .2647161 (mean)
               rebel           =    .3433756 (mean)
               warwin          =    .0973259 (mean)
               warloss         =    .0869074 (mean)
               rebelwin        =     .089078 (mean)
               rebelloss       =    .0288244 (mean)
               leveledu        =    2.059472 (mean)
               age             =          85
               teacher         =    .1338774 (mean)
               journalism      =    .0586907 (mean)
               law             =    .2217399 (mean)
               medicine        =    .0330787 (mean)
               religion        =    .0230943 (mean)
               activist        =    .1774614 (mean)
               careerpoli~n    =    .3346067 (mean)
               creative        =    .0633791 (mean)
               business        =    .0965445 (mean)
               aristocrat~r    =    .1131273 (mean)
               police          =     .009203 (mean)
               militaryca~r    =    .2426637 (mean)
               interpreter     =    .0037333 (mean)
               scienceeng      =    .0408925 (mean)
               gender          =    .9853273 (mean)
               married         =    .9576315 (mean)
               marriedinp~r    =    .9186491 (mean)
               divorced        =    .1479424 (mean)
               totalspouses    =    1.238062 (mean)
               childtotal      =    4.014412 (mean)
               parstability    =    .0711929 (mean)
               illegit         =    .0322105 (mean)
               royalty         =    .1449036 (mean)
               orphanbinary    =    .0331655 (mean)
               officet~1000    =    2.274386 (mean)
               yearssince~t    =    10.27783 (mean)
               y2              =    274.4617 (mean)
               y3              =    10610.92 (mean)
15._at       : milnoncombat    =    .1074839 (mean)
               combat          =    .2647161 (mean)
               rebel           =    .3433756 (mean)
               warwin          =    .0973259 (mean)
               warloss         =    .0869074 (mean)
               rebelwin        =     .089078 (mean)
               rebelloss       =    .0288244 (mean)
               leveledu        =    2.059472 (mean)
               age             =          90
               teacher         =    .1338774 (mean)
               journalism      =    .0586907 (mean)
               law             =    .2217399 (mean)
               medicine        =    .0330787 (mean)
               religion        =    .0230943 (mean)
               activist        =    .1774614 (mean)
               careerpoli~n    =    .3346067 (mean)
               creative        =    .0633791 (mean)
               business        =    .0965445 (mean)
               aristocrat~r    =    .1131273 (mean)
               police          =     .009203 (mean)
               militaryca~r    =    .2426637 (mean)
               interpreter     =    .0037333 (mean)
               scienceeng      =    .0408925 (mean)
               gender          =    .9853273 (mean)
               married         =    .9576315 (mean)
               marriedinp~r    =    .9186491 (mean)
               divorced        =    .1479424 (mean)
               totalspouses    =    1.238062 (mean)
               childtotal      =    4.014412 (mean)
               parstability    =    .0711929 (mean)
               illegit         =    .0322105 (mean)
               royalty         =    .1449036 (mean)
               orphanbinary    =    .0331655 (mean)
               officet~1000    =    2.274386 (mean)
               yearssince~t    =    10.27783 (mean)
               y2              =    274.4617 (mean)
               y3              =    10610.92 (mean)
16._at       : milnoncombat    =    .1074839 (mean)
               combat          =    .2647161 (mean)
               rebel           =    .3433756 (mean)
               warwin          =    .0973259 (mean)
               warloss         =    .0869074 (mean)
               rebelwin        =     .089078 (mean)
               rebelloss       =    .0288244 (mean)
               leveledu        =    2.059472 (mean)
               age             =          95
               teacher         =    .1338774 (mean)
               journalism      =    .0586907 (mean)
               law             =    .2217399 (mean)
               medicine        =    .0330787 (mean)
               religion        =    .0230943 (mean)
               activist        =    .1774614 (mean)
               careerpoli~n    =    .3346067 (mean)
               creative        =    .0633791 (mean)
               business        =    .0965445 (mean)
               aristocrat~r    =    .1131273 (mean)
               police          =     .009203 (mean)
               militaryca~r    =    .2426637 (mean)
               interpreter     =    .0037333 (mean)
               scienceeng      =    .0408925 (mean)
               gender          =    .9853273 (mean)
               married         =    .9576315 (mean)
               marriedinp~r    =    .9186491 (mean)
               divorced        =    .1479424 (mean)
               totalspouses    =    1.238062 (mean)
               childtotal      =    4.014412 (mean)
               parstability    =    .0711929 (mean)
               illegit         =    .0322105 (mean)
               royalty         =    .1449036 (mean)
               orphanbinary    =    .0331655 (mean)
               officet~1000    =    2.274386 (mean)
               yearssince~t    =    10.27783 (mean)
               y2              =    274.4617 (mean)
               y3              =    10610.92 (mean)
17._at       : milnoncombat    =    .1074839 (mean)
               combat          =    .2647161 (mean)
               rebel           =    .3433756 (mean)
               warwin          =    .0973259 (mean)
               warloss         =    .0869074 (mean)
               rebelwin        =     .089078 (mean)
               rebelloss       =    .0288244 (mean)
               leveledu        =    2.059472 (mean)
               age             =         100
               teacher         =    .1338774 (mean)
               journalism      =    .0586907 (mean)
               law             =    .2217399 (mean)
               medicine        =    .0330787 (mean)
               religion        =    .0230943 (mean)
               activist        =    .1774614 (mean)
               careerpoli~n    =    .3346067 (mean)
               creative        =    .0633791 (mean)
               business        =    .0965445 (mean)
               aristocrat~r    =    .1131273 (mean)
               police          =     .009203 (mean)
               militaryca~r    =    .2426637 (mean)
               interpreter     =    .0037333 (mean)
               scienceeng      =    .0408925 (mean)
               gender          =    .9853273 (mean)
               married         =    .9576315 (mean)
               marriedinp~r    =    .9186491 (mean)
               divorced        =    .1479424 (mean)
               totalspouses    =    1.238062 (mean)
               childtotal      =    4.014412 (mean)
               parstability    =    .0711929 (mean)
               illegit         =    .0322105 (mean)
               royalty         =    .1449036 (mean)
               orphanbinary    =    .0331655 (mean)
               officet~1000    =    2.274386 (mean)
               yearssince~t    =    10.27783 (mean)
               y2              =    274.4617 (mean)
               y3              =    10610.92 (mean)

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0924962     .01379     6.71   0.000     .0654682    .1195241
          2  |   .0968647    .012538     7.73   0.000     .0722906    .1214388
          3  |   .1014165   .0111737     9.08   0.000     .0795164    .1233165
          4  |    .106157   .0097074    10.94   0.000     .0871308    .1251832
          5  |   .1110918   .0081666    13.60   0.000     .0950856    .1270979
          6  |   .1162261    .006619    17.56   0.000     .1032531    .1291991
          7  |   .1215652   .0052379    23.21   0.000     .1112992    .1318313
          8  |   .1271144   .0044378    28.64   0.000     .1184165    .1358123
          9  |   .1328785   .0047976    27.70   0.000     .1234754    .1422817
         10  |   .1388624   .0063367    21.91   0.000     .1264428    .1512821
         11  |   .1450708    .008613    16.84   0.000     .1281896    .1619519
         12  |   .1515078   .0113372    13.36   0.000     .1292873    .1737283
         13  |   .1581776   .0143864    10.99   0.000     .1299808    .1863745
         14  |    .165084   .0177091     9.32   0.000     .1303747    .1997932
         15  |   .1722301   .0212821     8.09   0.000      .130518    .2139423
         16  |   .1796191   .0250938     7.16   0.000     .1304362    .2288021
         17  |   .1872534   .0291379     6.43   0.000     .1301441    .2443626
------------------------------------------------------------------------------

. marginsplot, recast(line) recastci(rarea) graphregion(fcolor(white) lcolor(white) ilcolor(white)) plotregion(fcolor(wh
> ite) lcolor(white) ilcolor(white)) ylabel(, nogrid) xtitle(, height(8)) ytitle("Probability of Military Conflict Initi
> ation", height(8)) title(" ",)

  Variables that uniquely identify margins: age

. graph save Graph "ReplicationFigure4_3.gph", replace
(file ReplicationFigure4_3.gph saved)

. 
. */ Figure 4.4 */
. 
. logit cwinit milnoncombat combat rebel warwin warloss rebelwin rebelloss leveledu teacher journalism law medicine reli
> gion activist careerpolitician creative business aristocratlandowner police militarycareer interpreter scienceeng gend
> er married marriedinpower divorced totalspouses childtotal parstability illegit royalty orphanbinary officetenure1000 
> c.age##aut yearssincemidinit y2 y3, robust cluster(leaderid)

Iteration 0:   log pseudolikelihood = -5579.3482  
Iteration 1:   log pseudolikelihood = -4648.9166  
Iteration 2:   log pseudolikelihood = -4536.6907  
Iteration 3:   log pseudolikelihood = -4534.5142  
Iteration 4:   log pseudolikelihood = -4534.4973  
Iteration 5:   log pseudolikelihood = -4534.4973  

Logistic regression                               Number of obs   =      11518
                                                  Wald chi2(39)   =     625.78
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -4534.4973                 Pseudo R2       =     0.1873

                                   (Std. Err. adjusted for 2258 clusters in leaderid)
-------------------------------------------------------------------------------------
                    |               Robust
             cwinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
       milnoncombat |   .6683736   .1828564     3.66   0.000     .3099816    1.026766
             combat |   .4020339   .1643149     2.45   0.014     .0799827    .7240851
              rebel |  -.0037284   .1319144    -0.03   0.977    -.2622758     .254819
             warwin |    .321256   .1649519     1.95   0.051    -.0020438    .6445558
            warloss |   .0947334   .1689298     0.56   0.575     -.236363    .4258297
           rebelwin |  -.0534546   .1512424    -0.35   0.724    -.3498842     .242975
          rebelloss |   .7191716   .2551059     2.82   0.005     .2191732     1.21917
           leveledu |   .0611705    .062439     0.98   0.327    -.0612076    .1835487
            teacher |  -.0263163   .1307141    -0.20   0.840    -.2825113    .2298787
         journalism |    -.13926   .1978502    -0.70   0.482    -.5270392    .2485192
                law |   -.124859   .1344821    -0.93   0.353    -.3884391    .1387211
           medicine |   -.592453   .2564605    -2.31   0.021    -1.095106   -.0897997
           religion |   .3423361   .4629809     0.74   0.460    -.5650897    1.249762
           activist |   .1295332   .1263937     1.02   0.305    -.1181939    .3772603
   careerpolitician |   -.052653   .1009911    -0.52   0.602     -.250592    .1452859
           creative |    .507229   .2373612     2.14   0.033     .0420095    .9724485
           business |  -.0893844   .1450818    -0.62   0.538    -.3737395    .1949707
aristocratlandowner |  -.2634239   .2181726    -1.21   0.227    -.6910342    .1641865
             police |    .177112   .4337545     0.41   0.683    -.6730312    1.027255
     militarycareer |  -.3049138   .1793227    -1.70   0.089    -.6563798    .0465521
        interpreter |  -1.008169   .3460616    -2.91   0.004    -1.686437   -.3299003
         scienceeng |   .2193358    .235424     0.93   0.352    -.2420868    .6807583
             gender |  -.4385821   .2794942    -1.57   0.117    -.9863807    .1092165
            married |   .0506115   .3403453     0.15   0.882     -.616453    .7176761
     marriedinpower |  -.1658685   .1998411    -0.83   0.407    -.5575499    .2258129
           divorced |  -.0252238   .1200054    -0.21   0.834      -.26043    .2099824
       totalspouses |  -.0246239   .0203074    -1.21   0.225    -.0644257    .0151779
         childtotal |   .0028536   .0057168     0.50   0.618    -.0083512    .0140584
       parstability |    .352168   .1976925     1.78   0.075    -.0353022    .7396381
            illegit |  -.5081869   .2274843    -2.23   0.025     -.954048   -.0623258
            royalty |  -.2117831   .2129801    -0.99   0.320    -.6292165    .2056504
       orphanbinary |  -.1227782   .2509306    -0.49   0.625    -.6145931    .3690368
   officetenure1000 |   .0216618   .0151335     1.43   0.152    -.0079993    .0513229
                age |   .0141251    .004896     2.89   0.004     .0045291    .0237211
              1.aut |    .747266   .4063471     1.84   0.066    -.0491596    1.543692
                    |
          aut#c.age |
                 1  |  -.0099832   .0073213    -1.36   0.173    -.0243327    .0043664
                    |
  yearssincemidinit |  -.3302873   .0228619   -14.45   0.000    -.3750958   -.2854787
                 y2 |   .0108866   .0013641     7.98   0.000     .0082129    .0135603
                 y3 |  -.0000993   .0000194    -5.11   0.000    -.0001373   -.0000612
              _cons |    -1.0131   .4948009    -2.05   0.041    -1.982892   -.0433086
-------------------------------------------------------------------------------------

. estimates store m4

. 
. margins aut, at(age=(20(5)100))  atmeans vsquish

Adjusted predictions                              Number of obs   =      11518
Model VCE    : Robust

Expression   : Pr(cwinit), predict()
1._at        : milnoncombat    =    .1074839 (mean)
               combat          =    .2647161 (mean)
               rebel           =    .3433756 (mean)
               warwin          =    .0973259 (mean)
               warloss         =    .0869074 (mean)
               rebelwin        =     .089078 (mean)
               rebelloss       =    .0288244 (mean)
               leveledu        =    2.059472 (mean)
               teacher         =    .1338774 (mean)
               journalism      =    .0586907 (mean)
               law             =    .2217399 (mean)
               medicine        =    .0330787 (mean)
               religion        =    .0230943 (mean)
               activist        =    .1774614 (mean)
               careerpoli~n    =    .3346067 (mean)
               creative        =    .0633791 (mean)
               business        =    .0965445 (mean)
               aristocrat~r    =    .1131273 (mean)
               police          =     .009203 (mean)
               militaryca~r    =    .2426637 (mean)
               interpreter     =    .0037333 (mean)
               scienceeng      =    .0408925 (mean)
               gender          =    .9853273 (mean)
               married         =    .9576315 (mean)
               marriedinp~r    =    .9186491 (mean)
               divorced        =    .1479424 (mean)
               totalspouses    =    1.238062 (mean)
               childtotal      =    4.014412 (mean)
               parstability    =    .0711929 (mean)
               illegit         =    .0322105 (mean)
               royalty         =    .1449036 (mean)
               orphanbinary    =    .0331655 (mean)
               officet~1000    =    2.274386 (mean)
               age             =          20
               0.aut           =    .6954332 (mean)
               1.aut           =    .3045668 (mean)
               yearssince~t    =    10.27783 (mean)
               y2              =    274.4617 (mean)
               y3              =    10610.92 (mean)
2._at        : milnoncombat    =    .1074839 (mean)
               combat          =    .2647161 (mean)
               rebel           =    .3433756 (mean)
               warwin          =    .0973259 (mean)
               warloss         =    .0869074 (mean)
               rebelwin        =     .089078 (mean)
               rebelloss       =    .0288244 (mean)
               leveledu        =    2.059472 (mean)
               teacher         =    .1338774 (mean)
               journalism      =    .0586907 (mean)
               law             =    .2217399 (mean)
               medicine        =    .0330787 (mean)
               religion        =    .0230943 (mean)
               activist        =    .1774614 (mean)
               careerpoli~n    =    .3346067 (mean)
               creative        =    .0633791 (mean)
               business        =    .0965445 (mean)
               aristocrat~r    =    .1131273 (mean)
               police          =     .009203 (mean)
               militaryca~r    =    .2426637 (mean)
               interpreter     =    .0037333 (mean)
               scienceeng      =    .0408925 (mean)
               gender          =    .9853273 (mean)
               married         =    .9576315 (mean)
               marriedinp~r    =    .9186491 (mean)
               divorced        =    .1479424 (mean)
               totalspouses    =    1.238062 (mean)
               childtotal      =    4.014412 (mean)
               parstability    =    .0711929 (mean)
               illegit         =    .0322105 (mean)
               royalty         =    .1449036 (mean)
               orphanbinary    =    .0331655 (mean)
               officet~1000    =    2.274386 (mean)
               age             =          25
               0.aut           =    .6954332 (mean)
               1.aut           =    .3045668 (mean)
               yearssince~t    =    10.27783 (mean)
               y2              =    274.4617 (mean)
               y3              =    10610.92 (mean)
3._at        : milnoncombat    =    .1074839 (mean)
               combat          =    .2647161 (mean)
               rebel           =    .3433756 (mean)
               warwin          =    .0973259 (mean)
               warloss         =    .0869074 (mean)
               rebelwin        =     .089078 (mean)
               rebelloss       =    .0288244 (mean)
               leveledu        =    2.059472 (mean)
               teacher         =    .1338774 (mean)
               journalism      =    .0586907 (mean)
               law             =    .2217399 (mean)
               medicine        =    .0330787 (mean)
               religion        =    .0230943 (mean)
               activist        =    .1774614 (mean)
               careerpoli~n    =    .3346067 (mean)
               creative        =    .0633791 (mean)
               business        =    .0965445 (mean)
               aristocrat~r    =    .1131273 (mean)
               police          =     .009203 (mean)
               militaryca~r    =    .2426637 (mean)
               interpreter     =    .0037333 (mean)
               scienceeng      =    .0408925 (mean)
               gender          =    .9853273 (mean)
               married         =    .9576315 (mean)
               marriedinp~r    =    .9186491 (mean)
               divorced        =    .1479424 (mean)
               totalspouses    =    1.238062 (mean)
               childtotal      =    4.014412 (mean)
               parstability    =    .0711929 (mean)
               illegit         =    .0322105 (mean)
               royalty         =    .1449036 (mean)
               orphanbinary    =    .0331655 (mean)
               officet~1000    =    2.274386 (mean)
               age             =          30
               0.aut           =    .6954332 (mean)
               1.aut           =    .3045668 (mean)
               yearssince~t    =    10.27783 (mean)
               y2              =    274.4617 (mean)
               y3              =    10610.92 (mean)
4._at        : milnoncombat    =    .1074839 (mean)
               combat          =    .2647161 (mean)
               rebel           =    .3433756 (mean)
               warwin          =    .0973259 (mean)
               warloss         =    .0869074 (mean)
               rebelwin        =     .089078 (mean)
               rebelloss       =    .0288244 (mean)
               leveledu        =    2.059472 (mean)
               teacher         =    .1338774 (mean)
               journalism      =    .0586907 (mean)
               law             =    .2217399 (mean)
               medicine        =    .0330787 (mean)
               religion        =    .0230943 (mean)
               activist        =    .1774614 (mean)
               careerpoli~n    =    .3346067 (mean)
               creative        =    .0633791 (mean)
               business        =    .0965445 (mean)
               aristocrat~r    =    .1131273 (mean)
               police          =     .009203 (mean)
               militaryca~r    =    .2426637 (mean)
               interpreter     =    .0037333 (mean)
               scienceeng      =    .0408925 (mean)
               gender          =    .9853273 (mean)
               married         =    .9576315 (mean)
               marriedinp~r    =    .9186491 (mean)
               divorced        =    .1479424 (mean)
               totalspouses    =    1.238062 (mean)
               childtotal      =    4.014412 (mean)
               parstability    =    .0711929 (mean)
               illegit         =    .0322105 (mean)
               royalty         =    .1449036 (mean)
               orphanbinary    =    .0331655 (mean)
               officet~1000    =    2.274386 (mean)
               age             =          35
               0.aut           =    .6954332 (mean)
               1.aut           =    .3045668 (mean)
               yearssince~t    =    10.27783 (mean)
               y2              =    274.4617 (mean)
               y3              =    10610.92 (mean)
5._at        : milnoncombat    =    .1074839 (mean)
               combat          =    .2647161 (mean)
               rebel           =    .3433756 (mean)
               warwin          =    .0973259 (mean)
               warloss         =    .0869074 (mean)
               rebelwin        =     .089078 (mean)
               rebelloss       =    .0288244 (mean)
               leveledu        =    2.059472 (mean)
               teacher         =    .1338774 (mean)
               journalism      =    .0586907 (mean)
               law             =    .2217399 (mean)
               medicine        =    .0330787 (mean)
               religion        =    .0230943 (mean)
               activist        =    .1774614 (mean)
               careerpoli~n    =    .3346067 (mean)
               creative        =    .0633791 (mean)
               business        =    .0965445 (mean)
               aristocrat~r    =    .1131273 (mean)
               police          =     .009203 (mean)
               militaryca~r    =    .2426637 (mean)
               interpreter     =    .0037333 (mean)
               scienceeng      =    .0408925 (mean)
               gender          =    .9853273 (mean)
               married         =    .9576315 (mean)
               marriedinp~r    =    .9186491 (mean)
               divorced        =    .1479424 (mean)
               totalspouses    =    1.238062 (mean)
               childtotal      =    4.014412 (mean)
               parstability    =    .0711929 (mean)
               illegit         =    .0322105 (mean)
               royalty         =    .1449036 (mean)
               orphanbinary    =    .0331655 (mean)
               officet~1000    =    2.274386 (mean)
               age             =          40
               0.aut           =    .6954332 (mean)
               1.aut           =    .3045668 (mean)
               yearssince~t    =    10.27783 (mean)
               y2              =    274.4617 (mean)
               y3              =    10610.92 (mean)
6._at        : milnoncombat    =    .1074839 (mean)
               combat          =    .2647161 (mean)
               rebel           =    .3433756 (mean)
               warwin          =    .0973259 (mean)
               warloss         =    .0869074 (mean)
               rebelwin        =     .089078 (mean)
               rebelloss       =    .0288244 (mean)
               leveledu        =    2.059472 (mean)
               teacher         =    .1338774 (mean)
               journalism      =    .0586907 (mean)
               law             =    .2217399 (mean)
               medicine        =    .0330787 (mean)
               religion        =    .0230943 (mean)
               activist        =    .1774614 (mean)
               careerpoli~n    =    .3346067 (mean)
               creative        =    .0633791 (mean)
               business        =    .0965445 (mean)
               aristocrat~r    =    .1131273 (mean)
               police          =     .009203 (mean)
               militaryca~r    =    .2426637 (mean)
               interpreter     =    .0037333 (mean)
               scienceeng      =    .0408925 (mean)
               gender          =    .9853273 (mean)
               married         =    .9576315 (mean)
               marriedinp~r    =    .9186491 (mean)
               divorced        =    .1479424 (mean)
               totalspouses    =    1.238062 (mean)
               childtotal      =    4.014412 (mean)
               parstability    =    .0711929 (mean)
               illegit         =    .0322105 (mean)
               royalty         =    .1449036 (mean)
               orphanbinary    =    .0331655 (mean)
               officet~1000    =    2.274386 (mean)
               age             =          45
               0.aut           =    .6954332 (mean)
               1.aut           =    .3045668 (mean)
               yearssince~t    =    10.27783 (mean)
               y2              =    274.4617 (mean)
               y3              =    10610.92 (mean)
7._at        : milnoncombat    =    .1074839 (mean)
               combat          =    .2647161 (mean)
               rebel           =    .3433756 (mean)
               warwin          =    .0973259 (mean)
               warloss         =    .0869074 (mean)
               rebelwin        =     .089078 (mean)
               rebelloss       =    .0288244 (mean)
               leveledu        =    2.059472 (mean)
               teacher         =    .1338774 (mean)
               journalism      =    .0586907 (mean)
               law             =    .2217399 (mean)
               medicine        =    .0330787 (mean)
               religion        =    .0230943 (mean)
               activist        =    .1774614 (mean)
               careerpoli~n    =    .3346067 (mean)
               creative        =    .0633791 (mean)
               business        =    .0965445 (mean)
               aristocrat~r    =    .1131273 (mean)
               police          =     .009203 (mean)
               militaryca~r    =    .2426637 (mean)
               interpreter     =    .0037333 (mean)
               scienceeng      =    .0408925 (mean)
               gender          =    .9853273 (mean)
               married         =    .9576315 (mean)
               marriedinp~r    =    .9186491 (mean)
               divorced        =    .1479424 (mean)
               totalspouses    =    1.238062 (mean)
               childtotal      =    4.014412 (mean)
               parstability    =    .0711929 (mean)
               illegit         =    .0322105 (mean)
               royalty         =    .1449036 (mean)
               orphanbinary    =    .0331655 (mean)
               officet~1000    =    2.274386 (mean)
               age             =          50
               0.aut           =    .6954332 (mean)
               1.aut           =    .3045668 (mean)
               yearssince~t    =    10.27783 (mean)
               y2              =    274.4617 (mean)
               y3              =    10610.92 (mean)
8._at        : milnoncombat    =    .1074839 (mean)
               combat          =    .2647161 (mean)
               rebel           =    .3433756 (mean)
               warwin          =    .0973259 (mean)
               warloss         =    .0869074 (mean)
               rebelwin        =     .089078 (mean)
               rebelloss       =    .0288244 (mean)
               leveledu        =    2.059472 (mean)
               teacher         =    .1338774 (mean)
               journalism      =    .0586907 (mean)
               law             =    .2217399 (mean)
               medicine        =    .0330787 (mean)
               religion        =    .0230943 (mean)
               activist        =    .1774614 (mean)
               careerpoli~n    =    .3346067 (mean)
               creative        =    .0633791 (mean)
               business        =    .0965445 (mean)
               aristocrat~r    =    .1131273 (mean)
               police          =     .009203 (mean)
               militaryca~r    =    .2426637 (mean)
               interpreter     =    .0037333 (mean)
               scienceeng      =    .0408925 (mean)
               gender          =    .9853273 (mean)
               married         =    .9576315 (mean)
               marriedinp~r    =    .9186491 (mean)
               divorced        =    .1479424 (mean)
               totalspouses    =    1.238062 (mean)
               childtotal      =    4.014412 (mean)
               parstability    =    .0711929 (mean)
               illegit         =    .0322105 (mean)
               royalty         =    .1449036 (mean)
               orphanbinary    =    .0331655 (mean)
               officet~1000    =    2.274386 (mean)
               age             =          55
               0.aut           =    .6954332 (mean)
               1.aut           =    .3045668 (mean)
               yearssince~t    =    10.27783 (mean)
               y2              =    274.4617 (mean)
               y3              =    10610.92 (mean)
9._at        : milnoncombat    =    .1074839 (mean)
               combat          =    .2647161 (mean)
               rebel           =    .3433756 (mean)
               warwin          =    .0973259 (mean)
               warloss         =    .0869074 (mean)
               rebelwin        =     .089078 (mean)
               rebelloss       =    .0288244 (mean)
               leveledu        =    2.059472 (mean)
               teacher         =    .1338774 (mean)
               journalism      =    .0586907 (mean)
               law             =    .2217399 (mean)
               medicine        =    .0330787 (mean)
               religion        =    .0230943 (mean)
               activist        =    .1774614 (mean)
               careerpoli~n    =    .3346067 (mean)
               creative        =    .0633791 (mean)
               business        =    .0965445 (mean)
               aristocrat~r    =    .1131273 (mean)
               police          =     .009203 (mean)
               militaryca~r    =    .2426637 (mean)
               interpreter     =    .0037333 (mean)
               scienceeng      =    .0408925 (mean)
               gender          =    .9853273 (mean)
               married         =    .9576315 (mean)
               marriedinp~r    =    .9186491 (mean)
               divorced        =    .1479424 (mean)
               totalspouses    =    1.238062 (mean)
               childtotal      =    4.014412 (mean)
               parstability    =    .0711929 (mean)
               illegit         =    .0322105 (mean)
               royalty         =    .1449036 (mean)
               orphanbinary    =    .0331655 (mean)
               officet~1000    =    2.274386 (mean)
               age             =          60
               0.aut           =    .6954332 (mean)
               1.aut           =    .3045668 (mean)
               yearssince~t    =    10.27783 (mean)
               y2              =    274.4617 (mean)
               y3              =    10610.92 (mean)
10._at       : milnoncombat    =    .1074839 (mean)
               combat          =    .2647161 (mean)
               rebel           =    .3433756 (mean)
               warwin          =    .0973259 (mean)
               warloss         =    .0869074 (mean)
               rebelwin        =     .089078 (mean)
               rebelloss       =    .0288244 (mean)
               leveledu        =    2.059472 (mean)
               teacher         =    .1338774 (mean)
               journalism      =    .0586907 (mean)
               law             =    .2217399 (mean)
               medicine        =    .0330787 (mean)
               religion        =    .0230943 (mean)
               activist        =    .1774614 (mean)
               careerpoli~n    =    .3346067 (mean)
               creative        =    .0633791 (mean)
               business        =    .0965445 (mean)
               aristocrat~r    =    .1131273 (mean)
               police          =     .009203 (mean)
               militaryca~r    =    .2426637 (mean)
               interpreter     =    .0037333 (mean)
               scienceeng      =    .0408925 (mean)
               gender          =    .9853273 (mean)
               married         =    .9576315 (mean)
               marriedinp~r    =    .9186491 (mean)
               divorced        =    .1479424 (mean)
               totalspouses    =    1.238062 (mean)
               childtotal      =    4.014412 (mean)
               parstability    =    .0711929 (mean)
               illegit         =    .0322105 (mean)
               royalty         =    .1449036 (mean)
               orphanbinary    =    .0331655 (mean)
               officet~1000    =    2.274386 (mean)
               age             =          65
               0.aut           =    .6954332 (mean)
               1.aut           =    .3045668 (mean)
               yearssince~t    =    10.27783 (mean)
               y2              =    274.4617 (mean)
               y3              =    10610.92 (mean)
11._at       : milnoncombat    =    .1074839 (mean)
               combat          =    .2647161 (mean)
               rebel           =    .3433756 (mean)
               warwin          =    .0973259 (mean)
               warloss         =    .0869074 (mean)
               rebelwin        =     .089078 (mean)
               rebelloss       =    .0288244 (mean)
               leveledu        =    2.059472 (mean)
               teacher         =    .1338774 (mean)
               journalism      =    .0586907 (mean)
               law             =    .2217399 (mean)
               medicine        =    .0330787 (mean)
               religion        =    .0230943 (mean)
               activist        =    .1774614 (mean)
               careerpoli~n    =    .3346067 (mean)
               creative        =    .0633791 (mean)
               business        =    .0965445 (mean)
               aristocrat~r    =    .1131273 (mean)
               police          =     .009203 (mean)
               militaryca~r    =    .2426637 (mean)
               interpreter     =    .0037333 (mean)
               scienceeng      =    .0408925 (mean)
               gender          =    .9853273 (mean)
               married         =    .9576315 (mean)
               marriedinp~r    =    .9186491 (mean)
               divorced        =    .1479424 (mean)
               totalspouses    =    1.238062 (mean)
               childtotal      =    4.014412 (mean)
               parstability    =    .0711929 (mean)
               illegit         =    .0322105 (mean)
               royalty         =    .1449036 (mean)
               orphanbinary    =    .0331655 (mean)
               officet~1000    =    2.274386 (mean)
               age             =          70
               0.aut           =    .6954332 (mean)
               1.aut           =    .3045668 (mean)
               yearssince~t    =    10.27783 (mean)
               y2              =    274.4617 (mean)
               y3              =    10610.92 (mean)
12._at       : milnoncombat    =    .1074839 (mean)
               combat          =    .2647161 (mean)
               rebel           =    .3433756 (mean)
               warwin          =    .0973259 (mean)
               warloss         =    .0869074 (mean)
               rebelwin        =     .089078 (mean)
               rebelloss       =    .0288244 (mean)
               leveledu        =    2.059472 (mean)
               teacher         =    .1338774 (mean)
               journalism      =    .0586907 (mean)
               law             =    .2217399 (mean)
               medicine        =    .0330787 (mean)
               religion        =    .0230943 (mean)
               activist        =    .1774614 (mean)
               careerpoli~n    =    .3346067 (mean)
               creative        =    .0633791 (mean)
               business        =    .0965445 (mean)
               aristocrat~r    =    .1131273 (mean)
               police          =     .009203 (mean)
               militaryca~r    =    .2426637 (mean)
               interpreter     =    .0037333 (mean)
               scienceeng      =    .0408925 (mean)
               gender          =    .9853273 (mean)
               married         =    .9576315 (mean)
               marriedinp~r    =    .9186491 (mean)
               divorced        =    .1479424 (mean)
               totalspouses    =    1.238062 (mean)
               childtotal      =    4.014412 (mean)
               parstability    =    .0711929 (mean)
               illegit         =    .0322105 (mean)
               royalty         =    .1449036 (mean)
               orphanbinary    =    .0331655 (mean)
               officet~1000    =    2.274386 (mean)
               age             =          75
               0.aut           =    .6954332 (mean)
               1.aut           =    .3045668 (mean)
               yearssince~t    =    10.27783 (mean)
               y2              =    274.4617 (mean)
               y3              =    10610.92 (mean)
13._at       : milnoncombat    =    .1074839 (mean)
               combat          =    .2647161 (mean)
               rebel           =    .3433756 (mean)
               warwin          =    .0973259 (mean)
               warloss         =    .0869074 (mean)
               rebelwin        =     .089078 (mean)
               rebelloss       =    .0288244 (mean)
               leveledu        =    2.059472 (mean)
               teacher         =    .1338774 (mean)
               journalism      =    .0586907 (mean)
               law             =    .2217399 (mean)
               medicine        =    .0330787 (mean)
               religion        =    .0230943 (mean)
               activist        =    .1774614 (mean)
               careerpoli~n    =    .3346067 (mean)
               creative        =    .0633791 (mean)
               business        =    .0965445 (mean)
               aristocrat~r    =    .1131273 (mean)
               police          =     .009203 (mean)
               militaryca~r    =    .2426637 (mean)
               interpreter     =    .0037333 (mean)
               scienceeng      =    .0408925 (mean)
               gender          =    .9853273 (mean)
               married         =    .9576315 (mean)
               marriedinp~r    =    .9186491 (mean)
               divorced        =    .1479424 (mean)
               totalspouses    =    1.238062 (mean)
               childtotal      =    4.014412 (mean)
               parstability    =    .0711929 (mean)
               illegit         =    .0322105 (mean)
               royalty         =    .1449036 (mean)
               orphanbinary    =    .0331655 (mean)
               officet~1000    =    2.274386 (mean)
               age             =          80
               0.aut           =    .6954332 (mean)
               1.aut           =    .3045668 (mean)
               yearssince~t    =    10.27783 (mean)
               y2              =    274.4617 (mean)
               y3              =    10610.92 (mean)
14._at       : milnoncombat    =    .1074839 (mean)
               combat          =    .2647161 (mean)
               rebel           =    .3433756 (mean)
               warwin          =    .0973259 (mean)
               warloss         =    .0869074 (mean)
               rebelwin        =     .089078 (mean)
               rebelloss       =    .0288244 (mean)
               leveledu        =    2.059472 (mean)
               teacher         =    .1338774 (mean)
               journalism      =    .0586907 (mean)
               law             =    .2217399 (mean)
               medicine        =    .0330787 (mean)
               religion        =    .0230943 (mean)
               activist        =    .1774614 (mean)
               careerpoli~n    =    .3346067 (mean)
               creative        =    .0633791 (mean)
               business        =    .0965445 (mean)
               aristocrat~r    =    .1131273 (mean)
               police          =     .009203 (mean)
               militaryca~r    =    .2426637 (mean)
               interpreter     =    .0037333 (mean)
               scienceeng      =    .0408925 (mean)
               gender          =    .9853273 (mean)
               married         =    .9576315 (mean)
               marriedinp~r    =    .9186491 (mean)
               divorced        =    .1479424 (mean)
               totalspouses    =    1.238062 (mean)
               childtotal      =    4.014412 (mean)
               parstability    =    .0711929 (mean)
               illegit         =    .0322105 (mean)
               royalty         =    .1449036 (mean)
               orphanbinary    =    .0331655 (mean)
               officet~1000    =    2.274386 (mean)
               age             =          85
               0.aut           =    .6954332 (mean)
               1.aut           =    .3045668 (mean)
               yearssince~t    =    10.27783 (mean)
               y2              =    274.4617 (mean)
               y3              =    10610.92 (mean)
15._at       : milnoncombat    =    .1074839 (mean)
               combat          =    .2647161 (mean)
               rebel           =    .3433756 (mean)
               warwin          =    .0973259 (mean)
               warloss         =    .0869074 (mean)
               rebelwin        =     .089078 (mean)
               rebelloss       =    .0288244 (mean)
               leveledu        =    2.059472 (mean)
               teacher         =    .1338774 (mean)
               journalism      =    .0586907 (mean)
               law             =    .2217399 (mean)
               medicine        =    .0330787 (mean)
               religion        =    .0230943 (mean)
               activist        =    .1774614 (mean)
               careerpoli~n    =    .3346067 (mean)
               creative        =    .0633791 (mean)
               business        =    .0965445 (mean)
               aristocrat~r    =    .1131273 (mean)
               police          =     .009203 (mean)
               militaryca~r    =    .2426637 (mean)
               interpreter     =    .0037333 (mean)
               scienceeng      =    .0408925 (mean)
               gender          =    .9853273 (mean)
               married         =    .9576315 (mean)
               marriedinp~r    =    .9186491 (mean)
               divorced        =    .1479424 (mean)
               totalspouses    =    1.238062 (mean)
               childtotal      =    4.014412 (mean)
               parstability    =    .0711929 (mean)
               illegit         =    .0322105 (mean)
               royalty         =    .1449036 (mean)
               orphanbinary    =    .0331655 (mean)
               officet~1000    =    2.274386 (mean)
               age             =          90
               0.aut           =    .6954332 (mean)
               1.aut           =    .3045668 (mean)
               yearssince~t    =    10.27783 (mean)
               y2              =    274.4617 (mean)
               y3              =    10610.92 (mean)
16._at       : milnoncombat    =    .1074839 (mean)
               combat          =    .2647161 (mean)
               rebel           =    .3433756 (mean)
               warwin          =    .0973259 (mean)
               warloss         =    .0869074 (mean)
               rebelwin        =     .089078 (mean)
               rebelloss       =    .0288244 (mean)
               leveledu        =    2.059472 (mean)
               teacher         =    .1338774 (mean)
               journalism      =    .0586907 (mean)
               law             =    .2217399 (mean)
               medicine        =    .0330787 (mean)
               religion        =    .0230943 (mean)
               activist        =    .1774614 (mean)
               careerpoli~n    =    .3346067 (mean)
               creative        =    .0633791 (mean)
               business        =    .0965445 (mean)
               aristocrat~r    =    .1131273 (mean)
               police          =     .009203 (mean)
               militaryca~r    =    .2426637 (mean)
               interpreter     =    .0037333 (mean)
               scienceeng      =    .0408925 (mean)
               gender          =    .9853273 (mean)
               married         =    .9576315 (mean)
               marriedinp~r    =    .9186491 (mean)
               divorced        =    .1479424 (mean)
               totalspouses    =    1.238062 (mean)
               childtotal      =    4.014412 (mean)
               parstability    =    .0711929 (mean)
               illegit         =    .0322105 (mean)
               royalty         =    .1449036 (mean)
               orphanbinary    =    .0331655 (mean)
               officet~1000    =    2.274386 (mean)
               age             =          95
               0.aut           =    .6954332 (mean)
               1.aut           =    .3045668 (mean)
               yearssince~t    =    10.27783 (mean)
               y2              =    274.4617 (mean)
               y3              =    10610.92 (mean)
17._at       : milnoncombat    =    .1074839 (mean)
               combat          =    .2647161 (mean)
               rebel           =    .3433756 (mean)
               warwin          =    .0973259 (mean)
               warloss         =    .0869074 (mean)
               rebelwin        =     .089078 (mean)
               rebelloss       =    .0288244 (mean)
               leveledu        =    2.059472 (mean)
               teacher         =    .1338774 (mean)
               journalism      =    .0586907 (mean)
               law             =    .2217399 (mean)
               medicine        =    .0330787 (mean)
               religion        =    .0230943 (mean)
               activist        =    .1774614 (mean)
               careerpoli~n    =    .3346067 (mean)
               creative        =    .0633791 (mean)
               business        =    .0965445 (mean)
               aristocrat~r    =    .1131273 (mean)
               police          =     .009203 (mean)
               militaryca~r    =    .2426637 (mean)
               interpreter     =    .0037333 (mean)
               scienceeng      =    .0408925 (mean)
               gender          =    .9853273 (mean)
               married         =    .9576315 (mean)
               marriedinp~r    =    .9186491 (mean)
               divorced        =    .1479424 (mean)
               totalspouses    =    1.238062 (mean)
               childtotal      =    4.014412 (mean)
               parstability    =    .0711929 (mean)
               illegit         =    .0322105 (mean)
               royalty         =    .1449036 (mean)
               orphanbinary    =    .0331655 (mean)
               officet~1000    =    2.274386 (mean)
               age             =         100
               0.aut           =    .6954332 (mean)
               1.aut           =    .3045668 (mean)
               yearssince~t    =    10.27783 (mean)
               y2              =    274.4617 (mean)
               y3              =    10610.92 (mean)

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     _at#aut |
        1 0  |   .0767764   .0130797     5.87   0.000     .0511406    .1024122
        1 1  |   .1257169   .0265998     4.73   0.000     .0735823    .1778516
        2 0  |   .0819345   .0121341     6.75   0.000     .0581521    .1057169
        2 1  |   .1280109   .0236543     5.41   0.000     .0816493    .1743725
        3 0  |   .0874063   .0110501     7.91   0.000     .0657485    .1090641
        3 1  |   .1303405   .0206991     6.30   0.000     .0897709      .17091
        4 0  |   .0932065   .0098415     9.47   0.000     .0739176    .1124954
        4 1  |    .132706   .0177894     7.46   0.000     .0978395    .1675725
        5 0  |   .0993496   .0085474    11.62   0.000     .0825971    .1161022
        5 1  |   .1351077   .0150263     8.99   0.000     .1056567    .1645588
        6 0  |   .1058504   .0072635    14.57   0.000     .0916143    .1200866
        6 1  |   .1375461   .0126017    10.91   0.000     .1128473    .1622449
        7 0  |   .1127233   .0062082    18.16   0.000     .1005554    .1248913
        7 1  |   .1400213   .0108618    12.89   0.000     .1187325    .1613101
        8 0  |   .1199826   .0057975    20.70   0.000     .1086196    .1313456
        8 1  |   .1425337   .0102921    13.85   0.000     .1223617    .1627058
        9 0  |   .1276421   .0064692    19.73   0.000     .1149628    .1403214
        9 1  |   .1450836    .011197    12.96   0.000      .123138    .1670293
       10 0  |   .1357152   .0082395    16.47   0.000     .1195661    .1518643
       10 1  |   .1476713   .0133854    11.03   0.000     .1214363    .1739063
       11 0  |   .1442145   .0108241    13.32   0.000     .1229996    .1654293
       11 1  |    .150297   .0164409     9.14   0.000     .1180735    .1825205
       12 0  |   .1531517   .0139976    10.94   0.000     .1257169    .1805866
       12 1  |    .152961   .0200469     7.63   0.000     .1136698    .1922521
       13 0  |   .1625376   .0176445     9.21   0.000      .127955    .1971202
       13 1  |   .1556635   .0240235     6.48   0.000     .1085783    .2027488
       14 0  |   .1723816   .0217096     7.94   0.000     .1298315    .2149317
       14 1  |   .1584049   .0282742     5.60   0.000     .1029885    .2138213
       15 0  |   .1826918   .0261655     6.98   0.000     .1314084    .2339752
       15 1  |   .1611854   .0327455     4.92   0.000     .0970054    .2253654
       16 0  |   .1934744   .0309963     6.24   0.000     .1327228     .254226
       16 1  |   .1640051   .0374068     4.38   0.000     .0906892     .237321
       17 0  |   .2047341   .0361907     5.66   0.000     .1338017    .2756665
       17 1  |   .1668644   .0422395     3.95   0.000     .0840765    .2496522
------------------------------------------------------------------------------

. marginsplot, recast(line) noci graphregion(fcolor(white) lcolor(white) ilcolor(white)) plotregion(fcolor(white) lcolor
> (white) ilcolor(white)) ylabel(, nogrid) xtitle(, height(8)) ytitle("Probability of Military Conflict Initiation", hei
> ght(8)) title(" ",) plot( , label("Non-Autocracy" "Autocracy"))

  Variables that uniquely identify margins: age aut

. graph save Graph "ReplicationFigure4_4.gph", replace
(file ReplicationFigure4_4.gph saved)

. 
. esttab m1 m2 m3 m4 using AppendixTableA_8.rtf, replace onecell se pr2 t(3) b(a3) scalars(ll) legend label collabels(no
> ne) varlabels(_cons Constant) star(* 0.10 ** 0.05 *** 0.01) mtitles("Model For Figure 4.1" "Model For Figure 4.2" "Mod
> el For Figure 4.3" "Model For Figure 4.4")
(output written to AppendixTableA_8.rtf)

. 
. clear

. 
. */ CHAPTER 5 */
. 
. */ Figure 5.1 */
. 
. use "Figure5_1.dta", clear
(Why Leaders Fight - Figure 5.1)

. 
. */ This dataset collapses the leader risk data by leader, as per the replication for Figure 2.3, but then also merges 
> in data on Childhood War Experience */
. 
. collapse (sum) war_no_tot (max) war_exp if adolescent == 0, by(year)

. 
. */ This collapses again by year to set up Figure 5.1 */
. 
. twoway line war_no_tot year if year > 1875 ||  qfit war_no_tot year if year > 1875, lp(dash) xlabel(#10) legend(order(
> 1 "Yearly Count of Childhood War Exposures" 2 "Quadratic Fit") rows(2) region(lcolor(white))) xtitle("Year", margin(me
> dsmall)) ytitle("Count of Childhood War Exposure", margin(small)) ylabel(, nogrid) scheme(s1mono) graphregion(fcolor(w
> hite) lcolor(white) ilcolor(white)) plotregion(fcolor(white) lcolor(white) ilcolor(white))

. graph save Graph "ReplicationFigure5_1.gph", replace
(file ReplicationFigure5_1.gph saved)

. 
. clear

. 
. */ Table 5.1 */
. 
. use WhyLeadersFightMonadicReplication.dta
(Why Leaders Fight - Monadic Replication)

. 
. */ First, regenerate data from Figure 2.3 */
. 
. */ Initial leader risk score */
. 
. logit cwinit milnoncombat combat rebel warwin warloss rebelwin rebelloss leveledu age teacher journalism law medicine 
> religion activist careerpolitician creative business aristocratlandowner police militarycareer scienceeng bluecollar g
> ender totalspouses married marriedinpower divorced childtotal parstability illegit royalty orphanbinary officetenure10
> 00 yearssincemidinit y2 y3, robust cluster(leaderid)

Iteration 0:   log pseudolikelihood = -5579.3482  
Iteration 1:   log pseudolikelihood = -4655.1691  
Iteration 2:   log pseudolikelihood = -4544.2096  
Iteration 3:   log pseudolikelihood = -4542.1008  
Iteration 4:   log pseudolikelihood = -4542.0842  
Iteration 5:   log pseudolikelihood = -4542.0842  

Logistic regression                               Number of obs   =      11518
                                                  Wald chi2(37)   =     591.29
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -4542.0842                 Pseudo R2       =     0.1859

                                   (Std. Err. adjusted for 2258 clusters in leaderid)
-------------------------------------------------------------------------------------
                    |               Robust
             cwinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
       milnoncombat |   .7086517   .1923578     3.68   0.000     .3316373    1.085666
             combat |   .4141407   .1701076     2.43   0.015     .0807359    .7475454
              rebel |   .0221961   .1299026     0.17   0.864    -.2324082    .2768005
             warwin |   .3218007   .1647528     1.95   0.051    -.0011088    .6447103
            warloss |   .0969283   .1696773     0.57   0.568    -.2356331    .4294898
           rebelwin |  -.0605395   .1510243    -0.40   0.689    -.3565416    .2354626
          rebelloss |   .7141976    .265052     2.69   0.007     .1947052     1.23369
           leveledu |   .0462606   .0663042     0.70   0.485    -.0836932    .1762144
                age |   .0102839   .0042992     2.39   0.017     .0018576    .0187102
            teacher |  -.0202248   .1296081    -0.16   0.876    -.2742519    .2338024
         journalism |  -.1359718   .1981908    -0.69   0.493    -.5244188    .2524751
                law |  -.1464982   .1310189    -1.12   0.264    -.4032904    .1102941
           medicine |  -.5529008   .2598877    -2.13   0.033    -1.062271   -.0435302
           religion |   .3620216   .4594624     0.79   0.431    -.5385081    1.262551
           activist |   .1473004   .1300168     1.13   0.257    -.1075279    .4021287
   careerpolitician |   -.057577   .1027884    -0.56   0.575    -.2590386    .1438846
           creative |    .524738   .2454673     2.14   0.033      .043631    1.005845
           business |  -.1006797   .1429239    -0.70   0.481    -.3808055     .179446
aristocratlandowner |  -.2511304   .2122983    -1.18   0.237    -.6672273    .1649666
             police |   .2307327   .4206547     0.55   0.583    -.5937354    1.055201
     militarycareer |   -.302673   .1919031    -1.58   0.115    -.6787962    .0734502
         scienceeng |   .2175702   .2413567     0.90   0.367    -.2554802    .6906205
         bluecollar |  -.0453714   .2007014    -0.23   0.821    -.4387389    .3479961
             gender |  -.3938837   .2863857    -1.38   0.169    -.9551893    .1674218
       totalspouses |  -.0202293   .0185804    -1.09   0.276    -.0566462    .0161875
            married |   .0396029   .3526166     0.11   0.911    -.6515128    .7307187
     marriedinpower |  -.1747878   .1984453    -0.88   0.378    -.5637335    .2141579
           divorced |   -.016662   .1196519    -0.14   0.889    -.2511755    .2178515
         childtotal |   .0039692   .0054605     0.73   0.467    -.0067332    .0146717
       parstability |   .3783283   .1988239     1.90   0.057    -.0113595    .7680161
            illegit |  -.5236266   .2319828    -2.26   0.024    -.9783045   -.0689486
            royalty |  -.2141826   .2088862    -1.03   0.305    -.6235919    .1952268
       orphanbinary |  -.0978384    .257739    -0.38   0.704    -.6029976    .4073208
   officetenure1000 |   .0243156   .0145865     1.67   0.096    -.0042734    .0529046
  yearssincemidinit |  -.3304432   .0229776   -14.38   0.000    -.3754785    -.285408
                 y2 |   .0108669   .0013562     8.01   0.000     .0082088    .0135249
                 y3 |   -.000099   .0000192    -5.14   0.000    -.0001367   -.0000612
              _cons |  -.7621431   .5220308    -1.46   0.144    -1.785305    .2610184
-------------------------------------------------------------------------------------

. predict leaderrisk if e(sample)
(option pr assumed; Pr(cwinit))
(52 missing values generated)

. la var leaderrisk "Leader Attribute Risk Score"

. 
. */ Initial system risk score */
. 
. logit cwinit cinc dem aut syscon irregular tau_lead fiveyearchallengelag lastwarwin lastwarloss lastwardraw yearssince
> midinit y2 y3, robust cluster(ccode)

Iteration 0:   log pseudolikelihood =  -5505.478  
Iteration 1:   log pseudolikelihood = -4388.0857  
Iteration 2:   log pseudolikelihood = -4239.6206  
Iteration 3:   log pseudolikelihood = -4230.9585  
Iteration 4:   log pseudolikelihood = -4230.8574  
Iteration 5:   log pseudolikelihood = -4230.8571  

Logistic regression                               Number of obs   =      11388
                                                  Wald chi2(13)   =     785.87
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -4230.8571                 Pseudo R2       =     0.2315

                                        (Std. Err. adjusted for 178 clusters in ccode)
--------------------------------------------------------------------------------------
                     |               Robust
              cwinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
                cinc |   9.404467   2.027024     4.64   0.000     5.431572    13.37736
                 dem |  -.2396804   .1588788    -1.51   0.131     -.551077    .0717163
                 aut |   .1679045   .1299729     1.29   0.196    -.0868377    .4226466
              syscon |  -3.486719   1.066383    -3.27   0.001    -5.576791   -1.396647
           irregular |   .2121851   .1162016     1.83   0.068    -.0155659    .4399361
            tau_lead |  -.0436213   .1617221    -0.27   0.787    -.3605907    .2733482
fiveyearchallengelag |   .6592767   .0783817     8.41   0.000     .5056514     .812902
          lastwarwin |   .7341308   .1532903     4.79   0.000     .4336873    1.034574
         lastwarloss |   .6475341   .1319239     4.91   0.000      .388968    .9061001
         lastwardraw |    .929418    .211505     4.39   0.000     .5148757     1.34396
   yearssincemidinit |  -.2690812   .0237309   -11.34   0.000    -.3155929   -.2225696
                  y2 |   .0092909    .001325     7.01   0.000     .0066941    .0118878
                  y3 |  -.0000889    .000018    -4.94   0.000    -.0001242   -.0000536
               _cons |  -.7252537   .3385474    -2.14   0.032    -1.388794   -.0617129
--------------------------------------------------------------------------------------

. predict systemrisk if e(sample)
(option pr assumed; Pr(cwinit))
(182 missing values generated)

. la var systemrisk "System Risk Score"

. 
. */ generate MID initiation summary data for creation upon data collapse */
. gen cwinit_sum = cwinit

. la var cwinit_sum "Number of MID Initiations"

. 
. collapse (mean) leaderrisk systemrisk (sum) cwinit_sum (min) ccode inyear gender (max) outyear, by(leaderid leadername
>  idacr)

. 
. */ Finally, create Table 5.1 */
. 
. gsort -cwinit_sum

. 
. list ccode idacr leaderid leadername leaderrisk cwinit_sum if gender==0 & cwinit_sum>0, table clean noobs

    ccode   idacr       leaderid              leadername   leader~k   cwinit~m  
      750     IND   LEAD.v1-8224           Indira Gandhi   .3883096          6  
      640     TUR   LEAD.v1-7351            Tansu Ciller   .3918573          5  
      750     IND   LEAD.v1-8233           Indira Gandhi   .4154811          4  
      710     CHN   LEAD.v1-7798                Tz'u Hsi   .1770745          3  
      666     ISR   LEAD.v1-7546              Golda Meir   .4723233          3  
       93     NIC   LEAD.v1-1066        Violeta Chamorro   .3225205          3  
      200     UKG   LEAD.v1-2884       Margaret Thatcher   .2386661          2  
      920     NEW   LEAD.v1-9067            Clark, Helen   .0003699          1  
      770     PAK   LEAD.v1-8332          Bhutto Benazir   .2395863          1  
       20     CAN    LEAD.v1-145            Kim Campbell   .3946868          1  
      160     ARG   LEAD.v1-2611           Peron, Isabel   .1390996          1  
      385     NOR   LEAD.v1-6034   Gro Harlem Brundtland   .2934225          1  
      771     BNG   LEAD.v1-8383             Khaleda Zia    .043691          1  

. 
. */ Note that duplicate leaders, e.g. leaders with multiple leader IDs because they served two different times, are ave
> raged */
. 
. */ Table created from the data above using Excel */
. 
. */ Figure 5.4 */
. 
. gsort -leaderrisk

. 
. list ccode idacr leaderid leadername leaderrisk cwinit_sum if gender==1 in 1/10, table clean noobs

    ccode   idacr       leaderid           leadername   leader~k   cwinit~m  
      710     CHN   LEAD.v1-7855        Deng Xiaoping   .6918895         16  
      630     IRN   LEAD.v1-7252   Ayatollah Khomeini   .6917507         64  
        2     USA     LEAD.v1-67        Ronald Reagan    .684314         16  
      344     CRO   LEAD.v1-4975       Franjo Tudjman   .6645699          5  
      220     FRN   LEAD.v1-3532      Georges Bidault    .644871          0  
      710     CHN   LEAD.v1-7849           Mao Zedong   .6333759         45  
      560     SAF   LEAD.v1-6904            Jan Smuts   .6267354          0  
      645     IRQ   LEAD.v1-7393       Hassan Al-Bakr   .6092224          8  
        2     USA     LEAD.v1-52      John F. Kennedy   .6027604          2  
        2     USA     LEAD.v1-61          Gerald Ford   .5843109          4  

. 
. */ Graph created from the data above using Excel */
. 
. */ Figure 5.3 */
. 
. gen female=0

. replace female=1 if gender==0
(33 real changes made)

. label define female 0 "Male", add

. label define female 1 "Female", add

. label values female female

. 
. graph box leaderrisk, over(female) box(1, fcolor(none) lcolor(black)) box(2, fcolor(none) lcolor(black)) medtype(marke
> r) medmarker(mcolor(black) msize(medium)) ytitle(Leader Risk Score) ytitle(, margin(medium)) yscale(noline) ylabel(, n
> ogrid) scheme(s1mono) graphregion(fcolor(white) ifcolor(white)) plotregion(fcolor(white) ifcolor(white) ilcolor(white)
> )

. graph save Graph "ReplicationFigure5_3.gph", replace
(file ReplicationFigure5_3.gph saved)

. 
. clear

. 
. */ Figure 5.2 */
. 
. use "WhyLeadersFightLEADDataset.dta"
(LEAD Dataset v1.3 - For Why Leaders Fight)

. 
. sort ccode year

. gen yr=year(startdate)
(71 missing values generated)

. order yr

. gen decade1870=0

. replace decade1870=1 if inyear>=1870 & inyear<1880
(592 real changes made)

. gen decade1880=0

. replace decade1880=1 if inyear>=1880 & inyear<1890
(654 real changes made)

. gen decade1890=0

. replace decade1890=1 if inyear>=1890 & inyear<1900
(558 real changes made)

. gen decade1900=0

. replace decade1900=1 if inyear>=1900 & inyear<1910
(583 real changes made)

. gen decade1910=0

. replace decade1910=1 if inyear>=1910 & inyear<1920
(772 real changes made)

. gen decade1920=0

. replace decade1920=1 if inyear>=1920 & inyear<1930
(991 real changes made)

. gen decade1930=0

. replace decade1930=1 if inyear>=1930 & inyear<1940
(962 real changes made)

. gen decade1940=0

. replace decade1940=1 if inyear>=1940 & inyear<1950
(1109 real changes made)

. gen decade1950=0

. replace decade1950=1 if inyear>=1950 & inyear<1960
(1359 real changes made)

. gen decade1960=0

. replace decade1960=1 if inyear>=1960 & inyear<1970
(1966 real changes made)

. gen decade1970=0

. replace decade1970=1 if inyear>=1970 & inyear<1980
(1571 real changes made)

. gen decade1980=0

. replace decade1980=1 if inyear>=1980 & inyear<1990
(1417 real changes made)

. gen decade1990=0

. replace decade1990=1 if inyear>=1990 & inyear<2000
(1741 real changes made)

. gen decade2000=0

. replace decade2000=1 if inyear>=2000 & inyear<2010
(383 real changes made)

. gen decade=.
(15454 missing values generated)

. replace decade=1 if decade1870==1
(592 real changes made)

. replace decade=2 if decade1880==1
(654 real changes made)

. replace decade=3 if decade1890==1
(558 real changes made)

. replace decade=4 if decade1900==1
(583 real changes made)

. replace decade=5 if decade1910==1
(772 real changes made)

. replace decade=6 if decade1920==1
(991 real changes made)

. replace decade=7 if decade1930==1
(962 real changes made)

. replace decade=8 if decade1940==1
(1109 real changes made)

. replace decade=9 if decade1950==1
(1359 real changes made)

. replace decade=10 if decade1960==1
(1966 real changes made)

. replace decade=11 if decade1970==1
(1571 real changes made)

. replace decade=12 if decade1980==1
(1417 real changes made)

. replace decade=13 if decade1990==1
(1741 real changes made)

. replace decade=14 if decade2000==1
(383 real changes made)

. 
. label define decades 2 "1880s", add

. label define decades 1 "1870s", add

. label define decades 3 "1890s", add

. label define decades 4 "1900s", add

. label define decades 5 "1910s", add

. label define decades 6 "1920s", add

. label define decades 7 "1930s", add

. label define decades 8 "1940s", add

. label define decades 9 "1950s", add

. label define decades 10 "1960s", add

. label define decades 11 "1970s", add

. label define decades 12 "1980s", add

. label define decades 13 "1990s", add

. label define decades 14 "2000s", add

. label values decade decades

. drop if decade==.
(796 observations deleted)

. 
. */ Generate number of women leaders per decade */
. */ NOTE: This just runs through 2004 */
. 
. gen uniquewoman=.
(14658 missing values generated)

. replace uniquewoman=3 if decade==10
(1966 real changes made)

. replace uniquewoman=4 if decade==11
(1571 real changes made)

. replace uniquewoman=8 if decade==12
(1417 real changes made)

. replace uniquewoman=19 if decade==13
(1741 real changes made)

. replace uniquewoman=7 if decade==14
(383 real changes made)

. 
. label var uniquewoman "Number of Women Leaders"

. label define uniquewoman 1 "Number of Women Leaders", add

. label values uniquewoman uniquewoman

. 
. */ Generate number of women leader-years per decade */
. 
. gen woman=0

. replace woman=1 if gender==0
(203 real changes made)

. label var woman "Number of Women Leader-Years"

. label define woman 1 "Women Leader-Years", add

. label values woman woman

. 
. graph bar (rawsum) woman (mean) uniquewoman if decade>=9, over(decade, label(labgap(small) alt)) ytitle(Number Per Dec
> ade) ytitle(, margin(medium)) ylabel(, nogrid) scheme(s1mono) graphregion(fcolor(white) ifcolor(white) ilcolor(white))
>  plotregion(fcolor(white) lcolor(white) ifcolor(white) ilcolor(white)) legend( label(1 "Women Leader-Years") label(2 "
> Women Leaders (Count)") region(lcolor(white)))

. graph save Graph "ReplicationFigure5_2.gph", replace
(file ReplicationFigure5_2.gph saved)

. 
. clear

. 
. */ CHAPTER 6 */
. 
. use "Figure6_1-6_2.dta", clear
(Why Leaders Fight - Figures 6.1 and 6.2)

. 
. */ This dataset merges the leader risk score data into war data based on Horowitz, Simpson, & Stam 2011. It is present
> ed as a separate dataset for simplicity */
. 
. */ Figure 6.1 */
. 
. graph bar (mean) wdl if endwarleader==1, over(risk_q, label(labgap(small) alt)) ytitle(Average War Outcome (0 = Loss, 
> 1 = Draw, 2 = Win)) ytitle(, margin(medium)) ylabel(, nogrid) scheme(s1mono) graphregion(fcolor(white) ifcolor(white) 
> ilcolor(white)) plotregion(fcolor(white) lcolor(white) ifcolor(white) ilcolor(white))

. 
. graph save Graph "ReplicationFigure6_1.gph", replace
(file ReplicationFigure6_1.gph saved)

. 
. */ Figure 6.2 */
. 
. graph bar (mean) finalcas, over(risk_q, label(labgap(small) alt)) ytitle(Average War Deaths) ytitle(, margin(medium)) 
> ylabel(, nogrid) scheme(s1mono) graphregion(fcolor(white) ifcolor(white) ilcolor(white)) plotregion(fcolor(white) lcol
> or(white) ifcolor(white) ilcolor(white))

. 
. graph save Graph "ReplicationFigure6_2.gph", replace
(file ReplicationFigure6_2.gph saved)

. 
. estimates clear

. clear

. 
. */ TECHNICAL APPENDIX */
. 
. use WhyLeadersFightMonadicReplication.dta
(Why Leaders Fight - Monadic Replication)

. 
. */ Appendix Table A.1, Replication and Extension of figure 2.3 in Why Leaders Fight */
. 
. */ A.1 - Model 1 */
. logit cwinit milnoncombat combat rebel warwin warloss rebelwin rebelloss leveledu age teacher journalism law medicine 
> religion activist careerpolitician creative business aristocratlandowner police militarycareer scienceeng bluecollar g
> ender totalspouses married marriedinpower divorced childtotal parstability illegit royalty orphanbinary officetenure10
> 00 yearssincemidinit y2 y3, robust cluster(leaderid)

Iteration 0:   log pseudolikelihood = -5579.3482  
Iteration 1:   log pseudolikelihood = -4655.1691  
Iteration 2:   log pseudolikelihood = -4544.2096  
Iteration 3:   log pseudolikelihood = -4542.1008  
Iteration 4:   log pseudolikelihood = -4542.0842  
Iteration 5:   log pseudolikelihood = -4542.0842  

Logistic regression                               Number of obs   =      11518
                                                  Wald chi2(37)   =     591.29
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -4542.0842                 Pseudo R2       =     0.1859

                                   (Std. Err. adjusted for 2258 clusters in leaderid)
-------------------------------------------------------------------------------------
                    |               Robust
             cwinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
       milnoncombat |   .7086517   .1923578     3.68   0.000     .3316373    1.085666
             combat |   .4141407   .1701076     2.43   0.015     .0807359    .7475454
              rebel |   .0221961   .1299026     0.17   0.864    -.2324082    .2768005
             warwin |   .3218007   .1647528     1.95   0.051    -.0011088    .6447103
            warloss |   .0969283   .1696773     0.57   0.568    -.2356331    .4294898
           rebelwin |  -.0605395   .1510243    -0.40   0.689    -.3565416    .2354626
          rebelloss |   .7141976    .265052     2.69   0.007     .1947052     1.23369
           leveledu |   .0462606   .0663042     0.70   0.485    -.0836932    .1762144
                age |   .0102839   .0042992     2.39   0.017     .0018576    .0187102
            teacher |  -.0202248   .1296081    -0.16   0.876    -.2742519    .2338024
         journalism |  -.1359718   .1981908    -0.69   0.493    -.5244188    .2524751
                law |  -.1464982   .1310189    -1.12   0.264    -.4032904    .1102941
           medicine |  -.5529008   .2598877    -2.13   0.033    -1.062271   -.0435302
           religion |   .3620216   .4594624     0.79   0.431    -.5385081    1.262551
           activist |   .1473004   .1300168     1.13   0.257    -.1075279    .4021287
   careerpolitician |   -.057577   .1027884    -0.56   0.575    -.2590386    .1438846
           creative |    .524738   .2454673     2.14   0.033      .043631    1.005845
           business |  -.1006797   .1429239    -0.70   0.481    -.3808055     .179446
aristocratlandowner |  -.2511304   .2122983    -1.18   0.237    -.6672273    .1649666
             police |   .2307327   .4206547     0.55   0.583    -.5937354    1.055201
     militarycareer |   -.302673   .1919031    -1.58   0.115    -.6787962    .0734502
         scienceeng |   .2175702   .2413567     0.90   0.367    -.2554802    .6906205
         bluecollar |  -.0453714   .2007014    -0.23   0.821    -.4387389    .3479961
             gender |  -.3938837   .2863857    -1.38   0.169    -.9551893    .1674218
       totalspouses |  -.0202293   .0185804    -1.09   0.276    -.0566462    .0161875
            married |   .0396029   .3526166     0.11   0.911    -.6515128    .7307187
     marriedinpower |  -.1747878   .1984453    -0.88   0.378    -.5637335    .2141579
           divorced |   -.016662   .1196519    -0.14   0.889    -.2511755    .2178515
         childtotal |   .0039692   .0054605     0.73   0.467    -.0067332    .0146717
       parstability |   .3783283   .1988239     1.90   0.057    -.0113595    .7680161
            illegit |  -.5236266   .2319828    -2.26   0.024    -.9783045   -.0689486
            royalty |  -.2141826   .2088862    -1.03   0.305    -.6235919    .1952268
       orphanbinary |  -.0978384    .257739    -0.38   0.704    -.6029976    .4073208
   officetenure1000 |   .0243156   .0145865     1.67   0.096    -.0042734    .0529046
  yearssincemidinit |  -.3304432   .0229776   -14.38   0.000    -.3754785    -.285408
                 y2 |   .0108669   .0013562     8.01   0.000     .0082088    .0135249
                 y3 |   -.000099   .0000192    -5.14   0.000    -.0001367   -.0000612
              _cons |  -.7621431   .5220308    -1.46   0.144    -1.785305    .2610184
-------------------------------------------------------------------------------------

. estimates store m1

. 
. */ A.1 - Model 2 */
. */ Drop leader years with multiple leaders per country but only one started MID (footnote 11, p. 65) */
. 
. duplicates tag ccode year, gen(duplicates)

Duplicates in terms of ccode year

. drop if duplicates>=1 & cwinit==0
(290 observations deleted)

. 
. logit cwinit milnoncombat combat rebel warwin warloss rebelwin rebelloss leveledu age teacher journalism law medicine 
> religion activist careerpolitician creative business aristocratlandowner police militarycareer scienceeng bluecollar g
> ender totalspouses married marriedinpower divorced childtotal parstability illegit royalty orphanbinary officetenure10
> 00 yearssincemidinit y2 y3, robust cluster(leaderid)

Iteration 0:   log pseudolikelihood = -5517.8213  
Iteration 1:   log pseudolikelihood = -4534.5169  
Iteration 2:   log pseudolikelihood = -4413.9783  
Iteration 3:   log pseudolikelihood = -4411.2258  
Iteration 4:   log pseudolikelihood = -4411.2002  
Iteration 5:   log pseudolikelihood = -4411.2002  

Logistic regression                               Number of obs   =      11228
                                                  Wald chi2(37)   =     610.40
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -4411.2002                 Pseudo R2       =     0.2006

                                   (Std. Err. adjusted for 2198 clusters in leaderid)
-------------------------------------------------------------------------------------
                    |               Robust
             cwinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
       milnoncombat |   .7001454   .1926608     3.63   0.000     .3225372    1.077754
             combat |   .3965963   .1711236     2.32   0.020     .0612002    .7319924
              rebel |  -.0007641   .1298411    -0.01   0.995     -.255248    .2537198
             warwin |   .3417209   .1662929     2.05   0.040     .0157927     .667649
            warloss |   .0817041   .1700805     0.48   0.631    -.2516476    .4150558
           rebelwin |  -.0629058   .1520099    -0.41   0.679    -.3608397    .2350281
          rebelloss |   .7263322   .2671179     2.72   0.007     .2027907    1.249874
           leveledu |   .0504287   .0665442     0.76   0.449    -.0799954    .1808529
                age |   .0116234   .0042935     2.71   0.007     .0032083    .0200385
            teacher |  -.0356362   .1307529    -0.27   0.785    -.2919071    .2206348
         journalism |  -.1076046   .2015737    -0.53   0.593    -.5026819    .2874726
                law |  -.1458733   .1321549    -1.10   0.270    -.4048921    .1131456
           medicine |  -.5608757   .2612311    -2.15   0.032    -1.072879   -.0488721
           religion |   .3825375   .4635961     0.83   0.409    -.5260941    1.291169
           activist |   .1444789    .130314     1.11   0.268    -.1109317    .3998896
   careerpolitician |  -.0711459   .1032166    -0.69   0.491    -.2734467    .1311549
           creative |   .5200647   .2465177     2.11   0.035     .0368989    1.003231
           business |  -.0750147   .1453941    -0.52   0.606     -.359982    .2099525
aristocratlandowner |  -.2426067    .210721    -1.15   0.250    -.6556123    .1703988
             police |   .1780995   .4261523     0.42   0.676    -.6571437    1.013343
     militarycareer |  -.2779319   .1921091    -1.45   0.148    -.6544587     .098595
         scienceeng |   .1919732   .2446475     0.78   0.433    -.2875271    .6714736
         bluecollar |  -.0856925   .1989153    -0.43   0.667    -.4755595    .3041744
             gender |  -.3614885   .2965828    -1.22   0.223      -.94278    .2198031
       totalspouses |  -.0201024   .0189253    -1.06   0.288    -.0571954    .0169906
            married |   .0790042   .3533159     0.22   0.823    -.6134821    .7714906
     marriedinpower |  -.1961954    .208214    -0.94   0.346    -.6042874    .2118965
           divorced |  -.0187879   .1219703    -0.15   0.878    -.2578452    .2202695
         childtotal |   .0038316    .005281     0.73   0.468     -.006519    .0141822
       parstability |   .3762534   .2011172     1.87   0.061    -.0179291     .770436
            illegit |  -.5520556   .2365379    -2.33   0.020    -1.015661   -.0884498
            royalty |  -.2273045   .2063344    -1.10   0.271    -.6317125    .1771034
       orphanbinary |  -.1070049   .2677744    -0.40   0.689    -.6318332    .4178233
   officetenure1000 |   .0129213   .0147116     0.88   0.380    -.0159128    .0417555
  yearssincemidinit |   -.353828   .0245309   -14.42   0.000    -.4019077   -.3057483
                 y2 |   .0118183   .0014802     7.98   0.000      .008917    .0147195
                 y3 |  -.0001092   .0000213    -5.11   0.000     -.000151   -.0000673
              _cons |  -.7329499   .5208455    -1.41   0.159    -1.753788    .2878885
-------------------------------------------------------------------------------------

. estimates store m2

. 
. clear

. 
. */ A.1 - Model 3 */
. */ Only allow 1 MID initiation per leader year */
. 
. use WhyLeadersFightMonadicReplication.dta
(Why Leaders Fight - Monadic Replication)

. 
. duplicates drop leaderid year if cwinit==1, force

Duplicates in terms of leaderid year

(576 observations deleted)

. 
. logit cwinit milnoncombat combat rebel warwin warloss rebelwin rebelloss leveledu age teacher journalism law medicine 
> religion activist careerpolitician creative business aristocratlandowner police militarycareer scienceeng bluecollar g
> ender totalspouses married marriedinpower divorced childtotal parstability illegit royalty orphanbinary officetenure10
> 00 yearssincemidinit y2 y3, robust cluster(leaderid)

Iteration 0:   log pseudolikelihood = -4549.4977  
Iteration 1:   log pseudolikelihood = -4054.5805  
Iteration 2:   log pseudolikelihood = -3998.9742  
Iteration 3:   log pseudolikelihood = -3998.5675  
Iteration 4:   log pseudolikelihood = -3998.5667  
Iteration 5:   log pseudolikelihood = -3998.5667  

Logistic regression                               Number of obs   =      10942
                                                  Wald chi2(37)   =     572.05
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -3998.5667                 Pseudo R2       =     0.1211

                                   (Std. Err. adjusted for 2258 clusters in leaderid)
-------------------------------------------------------------------------------------
                    |               Robust
             cwinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
       milnoncombat |   .5754046   .1454628     3.96   0.000     .2903028    .8605064
             combat |   .3710835    .149629     2.48   0.013     .0778161    .6643509
              rebel |   .0040066   .1056289     0.04   0.970    -.2030223    .2110354
             warwin |   .2561594   .1474853     1.74   0.082    -.0329064    .5452252
            warloss |    -.01217   .1352604    -0.09   0.928    -.2772756    .2529356
           rebelwin |  -.0003522   .1286955    -0.00   0.998    -.2525908    .2518864
          rebelloss |   .5938866   .2234817     2.66   0.008     .1558705    1.031903
           leveledu |   .0526405   .0547402     0.96   0.336    -.0546482    .1599293
                age |   .0061569    .003589     1.72   0.086    -.0008774    .0131911
            teacher |   .0268888   .1081392     0.25   0.804    -.1850601    .2388377
         journalism |  -.1254227   .1596586    -0.79   0.432    -.4383478    .1875025
                law |  -.0365774    .112869    -0.32   0.746    -.2577965    .1846417
           medicine |  -.3583609   .2422852    -1.48   0.139    -.8332312    .1165094
           religion |  -.1558746   .3568718    -0.44   0.662    -.8553304    .5435813
           activist |   .1901562   .1115304     1.70   0.088    -.0284394    .4087518
   careerpolitician |  -.0280595   .0834715    -0.34   0.737    -.1916607    .1355417
           creative |   .2022399   .1774945     1.14   0.255     -.145643    .5501228
           business |  -.0795795   .1218919    -0.65   0.514    -.3184834    .1593243
aristocratlandowner |  -.0843313   .2003096    -0.42   0.674    -.4769308    .3082682
             police |   .0186764   .3020656     0.06   0.951    -.5733614    .6107142
     militarycareer |    -.14112   .1517929    -0.93   0.353    -.4386286    .1563886
         scienceeng |   .2307103    .200111     1.15   0.249    -.1615001    .6229207
         bluecollar |  -.1288354   .1644857    -0.78   0.433    -.4512216    .1935507
             gender |   -.532933   .2596739    -2.05   0.040    -1.041885   -.0239814
       totalspouses |   -.021955   .0182405    -1.20   0.229    -.0577058    .0137957
            married |    .256601   .2580796     0.99   0.320    -.2492258    .7624277
     marriedinpower |  -.1746084   .1785827    -0.98   0.328    -.5246241    .1754072
           divorced |  -.0400644   .1046486    -0.38   0.702    -.2451719    .1650431
         childtotal |   .0048819   .0048831     1.00   0.317    -.0046888    .0144527
       parstability |   .3692258   .1737669     2.12   0.034      .028649    .7098026
            illegit |  -.4845222   .1948363    -2.49   0.013    -.8663942   -.1026501
            royalty |  -.1543617    .189235    -0.82   0.415    -.5252554     .216532
       orphanbinary |  -.0278302   .2312944    -0.12   0.904    -.4811589    .4254985
   officetenure1000 |   .0210083    .012451     1.69   0.092    -.0033953    .0454119
  yearssincemidinit |  -.2525695   .0184507   -13.69   0.000    -.2887322   -.2164068
                 y2 |   .0078795   .0010666     7.39   0.000      .005789      .00997
                 y3 |  -.0000691   .0000146    -4.72   0.000    -.0000978   -.0000404
              _cons |  -1.052669   .3714695    -2.83   0.005    -1.780736   -.3246019
-------------------------------------------------------------------------------------

. estimates store m3

. 
. */ Create Appendix Table A.1 */
. 
. esttab m1 m2 m3 using AppendixTableA_1.rtf, replace onecell se pr2 t(3) b(a3) scalars(ll) legend label collabels(none)
>  varlabels(_cons Constant) star(* 0.10 ** 0.05 *** 0.01) mtitles("Leader Risk Model" "Drop Duplicate Non-MID Leaders" 
> "One MID Per Leader Year")
(output written to AppendixTableA_1.rtf)

. 
. */ Appendix Table A.2, Leader Peace Years */
. 
. clear

. use WhyLeadersFightMonadicReplication.dta
(Why Leaders Fight - Monadic Replication)

. 
. */ Leader risk score with leader-based peace years */
. 
. logit cwinit milnoncombat combat rebel warwin warloss rebelwin rebelloss leveledu age teacher journalism law medicine 
> religion activist careerpolitician creative business aristocratlandowner police militarycareer scienceeng bluecollar g
> ender totalspouses married marriedinpower divorced childtotal parstability illegit royalty orphanbinary officetenure10
> 00 leaderpeaceyears leaderpeaceyears2 leaderpeaceyears3, robust cluster(leaderid)

Iteration 0:   log pseudolikelihood = -5579.3482  
Iteration 1:   log pseudolikelihood = -4858.3225  
Iteration 2:   log pseudolikelihood = -4783.2726  
Iteration 3:   log pseudolikelihood =  -4779.829  
Iteration 4:   log pseudolikelihood = -4779.5753  
Iteration 5:   log pseudolikelihood = -4779.5738  
Iteration 6:   log pseudolikelihood = -4779.5738  

Logistic regression                               Number of obs   =      11518
                                                  Wald chi2(37)   =     394.03
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -4779.5738                 Pseudo R2       =     0.1433

                                   (Std. Err. adjusted for 2258 clusters in leaderid)
-------------------------------------------------------------------------------------
                    |               Robust
             cwinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
       milnoncombat |   .8321556   .1943196     4.28   0.000     .4512963    1.213015
             combat |   .6433261   .1777011     3.62   0.000     .2950383    .9916139
              rebel |   .0971236   .1344201     0.72   0.470    -.1663349    .3605821
             warwin |   .2792704   .1811568     1.54   0.123    -.0757905    .6343313
            warloss |   .0074338   .1914001     0.04   0.969    -.3677035    .3825712
           rebelwin |  -.0301877   .1662558    -0.18   0.856    -.3560431    .2956677
          rebelloss |   .6279451   .2653911     2.37   0.018     .1077882    1.148102
           leveledu |   .0712777   .0688965     1.03   0.301     -.063757    .2063123
                age |   .0112497   .0045398     2.48   0.013     .0023518    .0201476
            teacher |   .1024852   .1321568     0.78   0.438    -.1565374    .3615078
         journalism |  -.1223745   .2082499    -0.59   0.557    -.5305369    .2857879
                law |  -.1653491   .1359716    -1.22   0.224    -.4318486    .1011504
           medicine |  -.3785194   .2640647    -1.43   0.152    -.8960768    .1390379
           religion |   .3258515   .4588316     0.71   0.478     -.573442    1.225145
           activist |   .2615337   .1357111     1.93   0.054    -.0044551    .5275226
   careerpolitician |   .0762065   .1059158     0.72   0.472    -.1313846    .2837976
           creative |   .5888422   .2542204     2.32   0.021     .0905793    1.087105
           business |  -.1157978   .1502868    -0.77   0.441    -.4103545    .1787588
aristocratlandowner |  -.2984026   .2375524    -1.26   0.209    -.7639967    .1671915
             police |   .1353614   .4441984     0.30   0.761    -.7352516    1.005974
     militarycareer |  -.3852428   .2005062    -1.92   0.055    -.7782278    .0077421
         scienceeng |   .2829222    .248375     1.14   0.255    -.2038838    .7697281
         bluecollar |  -.0036228   .2129734    -0.02   0.986    -.4210431    .4137974
             gender |  -.3076391   .3470588    -0.89   0.375    -.9878619    .3725838
       totalspouses |  -.0138348   .0204716    -0.68   0.499    -.0539584    .0262887
            married |   .0948221   .3772532     0.25   0.802    -.6445806    .8342249
     marriedinpower |  -.2840955   .2191384    -1.30   0.195    -.7135989    .1454079
           divorced |   .0692545   .1253492     0.55   0.581    -.1764254    .3149344
         childtotal |   .0046621   .0059051     0.79   0.430    -.0069116    .0162359
       parstability |   .4055547   .2029703     2.00   0.046     .0077401    .8033692
            illegit |  -.5881791   .2466207    -2.38   0.017    -1.071547   -.1048114
            royalty |  -.1206358   .2231756    -0.54   0.589    -.5580519    .3167803
       orphanbinary |  -.0804205   .2573484    -0.31   0.755    -.5848142    .4239731
   officetenure1000 |   .1351256   .0225044     6.00   0.000     .0910177    .1792335
   leaderpeaceyears |  -.6413029   .0510432   -12.56   0.000    -.7413457   -.5412601
  leaderpeaceyears2 |   .0504112   .0078831     6.39   0.000     .0349605    .0658619
  leaderpeaceyears3 |  -.0013233    .000316    -4.19   0.000    -.0019427    -.000704
              _cons |  -1.650555   .5605966    -2.94   0.003    -2.749304   -.5518058
-------------------------------------------------------------------------------------

. predict leaderrisk2 if e(sample)
(option pr assumed; Pr(cwinit))
(52 missing values generated)

. la var leaderrisk2 "Leader Attribute Risk Score (Leader Peace Years)"

. 
. */ generate MID initiation summary data for creation upon data collapse */
. gen cwinit_sum = cwinit

. la var cwinit_sum "Number of MID Initiations"

. 
. collapse (mean) leaderrisk2 (sum) cwinit_sum (min) ccode inyear (max) outyear, by(leaderid leadername idacr)

. 
. */ List top 10 conflict prone leaders, by leader risk score */
. gsort -leaderrisk2

. 
. list ccode idacr leaderid leadername leaderrisk2 cwinit_sum in 1/10, table clean noobs

    ccode   idacr       leaderid             leadername   leader~2   cwinit~m  
      630     IRN   LEAD.v1-7252     Ayatollah Khomeini    .678897         64  
      710     CHN   LEAD.v1-7855          Deng Xiaoping   .6763869         16  
      220     FRN   LEAD.v1-3586             Guy Mollet   .6739599          1  
      344     CRO   LEAD.v1-4975         Franjo Tudjman   .6494376          5  
      710     CHN   LEAD.v1-7849             Mao Zedong   .6342689         45  
        2     USA     LEAD.v1-67          Ronald Reagan   .6179188         16  
      220     FRN   LEAD.v1-3532        Georges Bidault   .6095592          0  
      365     RUS   LEAD.v1-5491   Konstantin Chernenko   .5583793          2  
      365     RUS   LEAD.v1-5476           Josef Stalin   .5547532         38  
      680     YPR   LEAD.v1-7633     Ali Nassir Hassani   .5425392          1  

. 
. */ Table A.2 generated by comparing the output above to Table 2.2 */
. 
. clear

. estimates clear

. 
. */ Table A.3: Combined Model */
. 
. use WhyLeadersFightMonadicReplication.dta
(Why Leaders Fight - Monadic Replication)

. 
. */ Initial leader risk score */
. 
. logit cwinit milnoncombat combat rebel warwin warloss rebelwin rebelloss leveledu age teacher journalism law medicine 
> religion activist careerpolitician creative business aristocratlandowner police militarycareer scienceeng bluecollar g
> ender totalspouses married marriedinpower divorced childtotal parstability illegit royalty orphanbinary officetenure10
> 00 yearssincemidinit y2 y3, robust cluster(leaderid)

Iteration 0:   log pseudolikelihood = -5579.3482  
Iteration 1:   log pseudolikelihood = -4655.1691  
Iteration 2:   log pseudolikelihood = -4544.2096  
Iteration 3:   log pseudolikelihood = -4542.1008  
Iteration 4:   log pseudolikelihood = -4542.0842  
Iteration 5:   log pseudolikelihood = -4542.0842  

Logistic regression                               Number of obs   =      11518
                                                  Wald chi2(37)   =     591.29
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -4542.0842                 Pseudo R2       =     0.1859

                                   (Std. Err. adjusted for 2258 clusters in leaderid)
-------------------------------------------------------------------------------------
                    |               Robust
             cwinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
       milnoncombat |   .7086517   .1923578     3.68   0.000     .3316373    1.085666
             combat |   .4141407   .1701076     2.43   0.015     .0807359    .7475454
              rebel |   .0221961   .1299026     0.17   0.864    -.2324082    .2768005
             warwin |   .3218007   .1647528     1.95   0.051    -.0011088    .6447103
            warloss |   .0969283   .1696773     0.57   0.568    -.2356331    .4294898
           rebelwin |  -.0605395   .1510243    -0.40   0.689    -.3565416    .2354626
          rebelloss |   .7141976    .265052     2.69   0.007     .1947052     1.23369
           leveledu |   .0462606   .0663042     0.70   0.485    -.0836932    .1762144
                age |   .0102839   .0042992     2.39   0.017     .0018576    .0187102
            teacher |  -.0202248   .1296081    -0.16   0.876    -.2742519    .2338024
         journalism |  -.1359718   .1981908    -0.69   0.493    -.5244188    .2524751
                law |  -.1464982   .1310189    -1.12   0.264    -.4032904    .1102941
           medicine |  -.5529008   .2598877    -2.13   0.033    -1.062271   -.0435302
           religion |   .3620216   .4594624     0.79   0.431    -.5385081    1.262551
           activist |   .1473004   .1300168     1.13   0.257    -.1075279    .4021287
   careerpolitician |   -.057577   .1027884    -0.56   0.575    -.2590386    .1438846
           creative |    .524738   .2454673     2.14   0.033      .043631    1.005845
           business |  -.1006797   .1429239    -0.70   0.481    -.3808055     .179446
aristocratlandowner |  -.2511304   .2122983    -1.18   0.237    -.6672273    .1649666
             police |   .2307327   .4206547     0.55   0.583    -.5937354    1.055201
     militarycareer |   -.302673   .1919031    -1.58   0.115    -.6787962    .0734502
         scienceeng |   .2175702   .2413567     0.90   0.367    -.2554802    .6906205
         bluecollar |  -.0453714   .2007014    -0.23   0.821    -.4387389    .3479961
             gender |  -.3938837   .2863857    -1.38   0.169    -.9551893    .1674218
       totalspouses |  -.0202293   .0185804    -1.09   0.276    -.0566462    .0161875
            married |   .0396029   .3526166     0.11   0.911    -.6515128    .7307187
     marriedinpower |  -.1747878   .1984453    -0.88   0.378    -.5637335    .2141579
           divorced |   -.016662   .1196519    -0.14   0.889    -.2511755    .2178515
         childtotal |   .0039692   .0054605     0.73   0.467    -.0067332    .0146717
       parstability |   .3783283   .1988239     1.90   0.057    -.0113595    .7680161
            illegit |  -.5236266   .2319828    -2.26   0.024    -.9783045   -.0689486
            royalty |  -.2141826   .2088862    -1.03   0.305    -.6235919    .1952268
       orphanbinary |  -.0978384    .257739    -0.38   0.704    -.6029976    .4073208
   officetenure1000 |   .0243156   .0145865     1.67   0.096    -.0042734    .0529046
  yearssincemidinit |  -.3304432   .0229776   -14.38   0.000    -.3754785    -.285408
                 y2 |   .0108669   .0013562     8.01   0.000     .0082088    .0135249
                 y3 |   -.000099   .0000192    -5.14   0.000    -.0001367   -.0000612
              _cons |  -.7621431   .5220308    -1.46   0.144    -1.785305    .2610184
-------------------------------------------------------------------------------------

. estimates store m1

. 
. */ Initial system risk score */
. 
. logit cwinit cinc dem aut syscon irregular tau_lead fiveyearchallengelag lastwarwin lastwarloss lastwardraw yearssince
> midinit y2 y3, robust cluster(ccode)

Iteration 0:   log pseudolikelihood =  -5505.478  
Iteration 1:   log pseudolikelihood = -4388.0857  
Iteration 2:   log pseudolikelihood = -4239.6206  
Iteration 3:   log pseudolikelihood = -4230.9585  
Iteration 4:   log pseudolikelihood = -4230.8574  
Iteration 5:   log pseudolikelihood = -4230.8571  

Logistic regression                               Number of obs   =      11388
                                                  Wald chi2(13)   =     785.87
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -4230.8571                 Pseudo R2       =     0.2315

                                        (Std. Err. adjusted for 178 clusters in ccode)
--------------------------------------------------------------------------------------
                     |               Robust
              cwinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
                cinc |   9.404467   2.027024     4.64   0.000     5.431572    13.37736
                 dem |  -.2396804   .1588788    -1.51   0.131     -.551077    .0717163
                 aut |   .1679045   .1299729     1.29   0.196    -.0868377    .4226466
              syscon |  -3.486719   1.066383    -3.27   0.001    -5.576791   -1.396647
           irregular |   .2121851   .1162016     1.83   0.068    -.0155659    .4399361
            tau_lead |  -.0436213   .1617221    -0.27   0.787    -.3605907    .2733482
fiveyearchallengelag |   .6592767   .0783817     8.41   0.000     .5056514     .812902
          lastwarwin |   .7341308   .1532903     4.79   0.000     .4336873    1.034574
         lastwarloss |   .6475341   .1319239     4.91   0.000      .388968    .9061001
         lastwardraw |    .929418    .211505     4.39   0.000     .5148757     1.34396
   yearssincemidinit |  -.2690812   .0237309   -11.34   0.000    -.3155929   -.2225696
                  y2 |   .0092909    .001325     7.01   0.000     .0066941    .0118878
                  y3 |  -.0000889    .000018    -4.94   0.000    -.0001242   -.0000536
               _cons |  -.7252537   .3385474    -2.14   0.032    -1.388794   -.0617129
--------------------------------------------------------------------------------------

. estimates store m2

. 
. */ Combined model */
. logit cwinit milnoncombat combat rebel warwin warloss rebelwin rebelloss leveledu age teacher journalism law medicine 
> religion activist careerpolitician creative business aristocratlandowner police militarycareer scienceeng bluecollar g
> ender totalspouses married marriedinpower divorced childtotal parstability illegit royalty orphanbinary officetenure10
> 00 cinc dem aut syscon irregular tau_lead fiveyearchallengelag lastwarwin lastwarloss lastwardraw yearssincemidinit y2
>  y3, robust cluster(leaderid)

Iteration 0:   log pseudolikelihood = -5490.6603  
Iteration 1:   log pseudolikelihood = -4327.4236  
Iteration 2:   log pseudolikelihood = -4176.5404  
Iteration 3:   log pseudolikelihood = -4167.9653  
Iteration 4:   log pseudolikelihood = -4167.9115  
Iteration 5:   log pseudolikelihood = -4167.9115  

Logistic regression                               Number of obs   =      11338
                                                  Wald chi2(47)   =    1039.43
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -4167.9115                 Pseudo R2       =     0.2409

                                    (Std. Err. adjusted for 2235 clusters in leaderid)
--------------------------------------------------------------------------------------
                     |               Robust
              cwinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
        milnoncombat |   .4101683   .1617034     2.54   0.011     .0932354    .7271012
              combat |   .2985721   .1752971     1.70   0.089     -.045004    .6421481
               rebel |   .2522123   .1184494     2.13   0.033     .0200557    .4843689
              warwin |   .0006812   .1659406     0.00   0.997    -.3245563    .3259187
             warloss |   .0887751   .1497235     0.59   0.553    -.2046775    .3822277
            rebelwin |  -.1885449    .140334    -1.34   0.179    -.4635944    .0865047
           rebelloss |   .1473398   .2002645     0.74   0.462    -.2451714     .539851
            leveledu |   .0378669   .0569222     0.67   0.506    -.0736985    .1494324
                 age |  -.0000685   .0038175    -0.02   0.986    -.0075507    .0074137
             teacher |   .1347857   .1127995     1.19   0.232    -.0862973    .3558688
          journalism |  -.0414637   .1752797    -0.24   0.813    -.3850055    .3020781
                 law |   .0251939   .1167089     0.22   0.829    -.2035513    .2539391
            medicine |   .0125353   .2449864     0.05   0.959    -.4676292    .4926997
            religion |   .5951726   .3851851     1.55   0.122    -.1597763    1.350121
            activist |   .2453443   .1186551     2.07   0.039     .0127845     .477904
    careerpolitician |   .0501785   .0944043     0.53   0.595    -.1348505    .2352075
            creative |   .3577851   .1787673     2.00   0.045     .0074076    .7081626
            business |   .1121731   .1329576     0.84   0.399    -.1484191    .3727653
 aristocratlandowner |  -.1426294   .1862308    -0.77   0.444     -.507635    .2223762
              police |   .4165465   .3019423     1.38   0.168    -.1752495    1.008342
      militarycareer |   -.075107   .1637579    -0.46   0.646    -.3960666    .2458526
          scienceeng |   .2878494   .2059195     1.40   0.162    -.1157454    .6914441
          bluecollar |   .0542465    .187674     0.29   0.773    -.3135877    .4220807
              gender |   .0768951   .3668985     0.21   0.834    -.6422127    .7960029
        totalspouses |  -.0085361   .0166034    -0.51   0.607     -.041078    .0240059
             married |  -.1234297    .274407    -0.45   0.653    -.6612576    .4143981
      marriedinpower |   .1360991   .1799911     0.76   0.450    -.2166769    .4888752
            divorced |    .066956   .1135961     0.59   0.556    -.1556883    .2896004
          childtotal |   .0024705   .0054421     0.45   0.650    -.0081958    .0131368
        parstability |    .127999   .1813355     0.71   0.480    -.2274121    .4834101
             illegit |  -.2752161   .2181106    -1.26   0.207    -.7027049    .1522727
             royalty |   -.026839   .1831037    -0.15   0.883    -.3857156    .3320376
        orphanbinary |   .0121383   .2309607     0.05   0.958    -.4405364     .464813
    officetenure1000 |   .0176064   .0151586     1.16   0.245    -.0121039    .0473166
                cinc |   9.301611   1.267916     7.34   0.000     6.816541    11.78668
                 dem |  -.1615137   .1130206    -1.43   0.153    -.3830301    .0600027
                 aut |   .1175824   .1025972     1.15   0.252    -.0835044    .3186691
              syscon |  -2.729052   1.002359    -2.72   0.006    -4.693638   -.7644649
           irregular |   .0770317   .1142683     0.67   0.500      -.14693    .3009935
            tau_lead |   .1156476   .1263796     0.92   0.360    -.1320518     .363347
fiveyearchallengelag |   .6448136   .0694182     9.29   0.000     .5087564    .7808708
          lastwarwin |   .7426471   .1223187     6.07   0.000     .5029068    .9823874
         lastwarloss |    .627465   .1126098     5.57   0.000     .4067539    .8481762
         lastwardraw |   .7974165    .155939     5.11   0.000     .4917816    1.103051
   yearssincemidinit |  -.2564191   .0201546   -12.72   0.000    -.2959214   -.2169167
                  y2 |   .0087727   .0011758     7.46   0.000     .0064681    .0110773
                  y3 |  -.0000828   .0000168    -4.91   0.000    -.0001158   -.0000498
               _cons |  -1.493621   .6060828    -2.46   0.014    -2.681521   -.3057204
--------------------------------------------------------------------------------------

. estimates store m3

. 
. */ Output table for online appendix */
. 
. esttab m1 m2 m3 using AppendixTableA_3.rtf, replace onecell se pr2 t(3) b(a3) scalars(ll) legend label collabels(none)
>  varlabels(_cons Constant) star(* 0.10 ** 0.05 *** 0.01) mtitles("Leader Risk Model" "System Risk Model" "Combined Mod
> el")
(output written to AppendixTableA_3.rtf)

. 
. estimates clear

. 
. */ Table A.4 - Alternative DVs */
. 
. */Initial leader risk score with use of force DV + use of force leader-specific splines */
. logit force2dv milnoncombat combat rebel warwin warloss rebelwin rebelloss leveledu age teacher journalism law medicin
> e religion activist careerpolitician creative business aristocratlandowner police militarycareer scienceeng bluecollar
>  gender totalspouses married marriedinpower divorced childtotal parstability illegit royalty orphanbinary officetenure
> 1000 noncountryforceyrs noncountryforceyrs2 noncountryforceyrs3, robust cluster(leaderid)

Iteration 0:   log pseudolikelihood = -2502.6709  
Iteration 1:   log pseudolikelihood = -2290.7991  
Iteration 2:   log pseudolikelihood =  -2226.821  
Iteration 3:   log pseudolikelihood = -2225.9991  
Iteration 4:   log pseudolikelihood = -2225.9802  
Iteration 5:   log pseudolikelihood = -2225.9801  

Logistic regression                               Number of obs   =      11518
                                                  Wald chi2(37)   =     443.34
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -2225.9801                 Pseudo R2       =     0.1106

                                   (Std. Err. adjusted for 2258 clusters in leaderid)
-------------------------------------------------------------------------------------
                    |               Robust
           force2dv |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
       milnoncombat |   .4929248   .1625039     3.03   0.002     .1744231    .8114266
             combat |   .4159586   .2011294     2.07   0.039     .0217522    .8101649
              rebel |   .2626947   .1413014     1.86   0.063     -.014251    .5396403
             warwin |   .2034727   .1885229     1.08   0.280    -.1660253    .5729707
            warloss |  -.2734038   .1872423    -1.46   0.144    -.6403919    .0935843
           rebelwin |   .1011858    .154394     0.66   0.512    -.2014208    .4037924
          rebelloss |   .2912219   .1833188     1.59   0.112    -.0680764    .6505201
           leveledu |   .1178021    .069448     1.70   0.090    -.0183135    .2539178
                age |   .0039545   .0045934     0.86   0.389    -.0050485    .0129574
            teacher |   .1268944   .1359933     0.93   0.351    -.1396475    .3934363
         journalism |  -.2107951   .2028125    -1.04   0.299    -.6083002    .1867101
                law |  -.0743376   .1589549    -0.47   0.640    -.3858835    .2372083
           medicine |   .0817319   .4092399     0.20   0.842    -.7203636    .8838274
           religion |  -1.004386   .4558713    -2.20   0.028    -1.897877   -.1108943
           activist |   .4224013   .1314508     3.21   0.001     .1647625    .6800402
   careerpolitician |  -.1120406   .1135184    -0.99   0.324    -.3345326    .1104514
           creative |    .085025   .2160756     0.39   0.694    -.3384753    .5085254
           business |  -.0973175   .1811175    -0.54   0.591    -.4523014    .2576663
aristocratlandowner |  -.0617177   .2242352    -0.28   0.783    -.5012105    .3777752
             police |   .4576097   .3268335     1.40   0.161    -.1829721    1.098192
     militarycareer |   .0932341   .1585675     0.59   0.557    -.2175525    .4040207
         scienceeng |   -.229565   .2581637    -0.89   0.374    -.7355565    .2764265
         bluecollar |  -.2689852   .1931504    -1.39   0.164    -.6475529    .1095826
             gender |  -.4711025   .3497865    -1.35   0.178    -1.156672    .2144664
       totalspouses |  -.0213324    .019237    -1.11   0.267    -.0590361    .0163714
            married |  -.6362572   .3283168    -1.94   0.053    -1.279746    .0072318
     marriedinpower |   .2710725   .2110454     1.28   0.199    -.1425688    .6847138
           divorced |   .0841089   .1269693     0.66   0.508    -.1647463    .3329641
         childtotal |  -.0044189   .0112536    -0.39   0.695    -.0264756    .0176378
       parstability |   .2863476   .1942185     1.47   0.140    -.0943137    .6670088
            illegit |  -.6550968   .2793475    -2.35   0.019    -1.202608   -.1075858
            royalty |   .1995243   .2088991     0.96   0.340    -.2099103    .6089589
       orphanbinary |   .1053156   .3074654     0.34   0.732    -.4973055    .7079367
   officetenure1000 |  -.0100424   .0172616    -0.58   0.561    -.0438746    .0237897
 noncountryforceyrs |  -.1546122   .0133175   -11.61   0.000    -.1807141   -.1285103
noncountryforceyrs2 |   .0031376   .0004272     7.34   0.000     .0023003    .0039748
noncountryforceyrs3 |  -.0000186   3.53e-06    -5.27   0.000    -.0000255   -.0000117
              _cons |  -1.791426   .4726318    -3.79   0.000    -2.717767   -.8650844
-------------------------------------------------------------------------------------

. predict leaderrisk if e(sample)
(option pr assumed; Pr(force2dv))
(52 missing values generated)

. la var leaderrisk "Leader Attribute Risk Score"

. 
. */ Initial system risk score with use of force DV */
. logit force2dv cinc dem aut syscon irregular tau_lead fiveyearchallengelag lastwarwin lastwarloss lastwardraw noncount
> ryforceyrs noncountryforceyrs2 noncountryforceyrs3, robust cluster(ccode)

Iteration 0:   log pseudolikelihood = -2466.9519  
Iteration 1:   log pseudolikelihood = -2231.3453  
Iteration 2:   log pseudolikelihood = -2161.3185  
Iteration 3:   log pseudolikelihood = -2160.5418  
Iteration 4:   log pseudolikelihood = -2160.5314  
Iteration 5:   log pseudolikelihood = -2160.5314  

Logistic regression                               Number of obs   =      11388
                                                  Wald chi2(13)   =     334.80
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -2160.5314                 Pseudo R2       =     0.1242

                                        (Std. Err. adjusted for 178 clusters in ccode)
--------------------------------------------------------------------------------------
                     |               Robust
            force2dv |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
                cinc |   1.710468   .9892129     1.73   0.084    -.2283538     3.64929
                 dem |   .0006046   .1937918     0.00   0.998    -.3792203    .3804296
                 aut |   .2065354    .125219     1.65   0.099    -.0388894    .4519601
              syscon |  -.5568388   1.109244    -0.50   0.616    -2.730918     1.61724
           irregular |   .4115373    .136433     3.02   0.003     .1441336    .6789411
            tau_lead |  -.1970694   .1797568    -1.10   0.273    -.5493863    .1552474
fiveyearchallengelag |   .5356412   .1257266     4.26   0.000     .2892216    .7820608
          lastwarwin |   .8089343   .1801042     4.49   0.000     .4559365    1.161932
         lastwarloss |   .7901387   .1555468     5.08   0.000     .4852724    1.095005
         lastwardraw |   .8776338   .2249552     3.90   0.000     .4367296    1.318538
  noncountryforceyrs |  -.1343877   .0149017    -9.02   0.000    -.1635945   -.1051808
 noncountryforceyrs2 |   .0026328   .0004409     5.97   0.000     .0017686     .003497
 noncountryforceyrs3 |  -.0000152   3.50e-06    -4.34   0.000     -.000022   -8.33e-06
               _cons |  -2.859105   .3835148    -7.46   0.000     -3.61078    -2.10743
--------------------------------------------------------------------------------------

. predict systemrisk if e(sample)
(option pr assumed; Pr(force2dv))
(182 missing values generated)

. la var systemrisk "System Risk Score"

. 
. */ generate force summary data for creation upon data collapse */
. gen force_sum = force2dv

. la var force_sum "Number of Conflicts Where Both Sides Used Force"

. 
. collapse (mean) leaderrisk systemrisk (sum) force_sum (min) ccode inyear (max) outyear, by(leaderid leadername idacr)

. 
. */ List top 2% of most dangerous leaders in reality based on DV and compare how leader model v. system model predict t
> heir risk % */
. gsort -leaderid

. gsort -force_sum

. 
. xtile pct = leaderrisk, nq(100)

. xtile pct2 = systemrisk, nq(100)

. xtile pct3 = force_sum, nq(100)

. replace pct=pct-1
(2258 real changes made)

. replace pct2=pct2-1
(2258 real changes made)

. replace pct3=pct3-1
(2282 real changes made)

. list ccode idacr leaderid leadername pct pct2 pct3 force_sum if pct3>=98, table clean noobs

    ccode   idacr       leaderid                    leadername   pct   pct2   pct3   force_~m  
      710     CHN   LEAD.v1-7849                    Mao Zedong    99     99     99         17  
      731     PRK   LEAD.v1-7942                   Kim Il-Sung    99     93     99         16  
      255     GMY   LEAD.v1-4354                  Adolf Hitler    94     98     99         15  
      365     RUS   LEAD.v1-5476                  Josef Stalin    98     98     99          9  
      770     PAK   LEAD.v1-8317                     Ayub Khan    96     99     99          8  
      530     ETH   LEAD.v1-6850              Mengistu Marriam    99     89     99          7  
      750     IND   LEAD.v1-8212              Jawaharlal Nehru    89     91     99          7  
      365     RUS   LEAD.v1-5470                   Nicholas II    83     84     99          6  
      652     SYR   LEAD.v1-7468            Nureddin al-Atassi    98     99     99          6  
      500     UGA   LEAD.v1-6733               Yoweri Museveni    98     98     99          6  
      652     SYR   LEAD.v1-7474                Hafez Al-Assad    93     91     99          6  
      710     CHN   LEAD.v1-7855                 Deng Xiaoping    99     99     99          6  
      255     GMY   LEAD.v1-4306                    Wilhelm II    85     79     99          6  
      552     ZIM   LEAD.v1-6883                     Ian Smith    76     70     98          5  
      520     SOM   LEAD.v1-6808                    Osman Daar    87     71     98          5  
      500     UGA   LEAD.v1-6715                      Idi Amin    95     91     98          5  
      630     IRN   LEAD.v1-7252            Ayatollah Khomeini    82     99     98          5  
      325     ITA   LEAD.v1-4720              Benito Mussolini    89     92     98          5  
      651     EGY   LEAD.v1-7420            Gamal Abdel Nasser    99     99     98          5  
      520     SOM   LEAD.v1-6814                    Siad Barre    97     89     98          5  
      600     MOR   LEAD.v1-7138                     Hassan II    59     86     98          5  
      490     DRC   LEAD.v1-6703              Mobutu Sese Seko    72     92     98          5  
      645     IRQ   LEAD.v1-7396                Saddam Hussein    93     92     98          5  
      365     RUS   LEAD.v1-5473                Vladimir Lenin    92     99     98          5  
      630     IRN   LEAD.v1-7249                 Mohammad Reza    86     96     98          5  
      652     SYR   LEAD.v1-7465                 Amin al-Hafez    95     99     98          5  
      850     INS   LEAD.v1-8860                       Sukarno    86     59     98          4  
      713     TAW   LEAD.v1-7921               Chiang Kai-shek    93     83     98          4  
      290     POL   LEAD.v1-4399               Jozef Pilsudski    99     97     98          4  
      663     JOR   LEAD.v1-7525   Hussein Ibn Talal El-Hashim    54     78     98          4  
      666     ISR   LEAD.v1-7531                    Ben Gurion    99     93     98          4  
      471     CAO   LEAD.v1-6586                     Paul Biya    71     39     98          4  
      750     IND   LEAD.v1-8236                  Rajiv Gandhi    76     97     98          4  
      770     PAK   LEAD.v1-8326           Muhammad Zia-ul-Haq    94     98     98          4  
      812     LAO   LEAD.v1-8710            Kaysone Phomvihane    90     98     98          4  
      816     DRV   LEAD.v1-8737                   Ho Chi Minh    94     89     98          4  
      620     LIB   LEAD.v1-7186               Muammar Qaddafi    93     93     98          4  
      365     RUS   LEAD.v1-5497                 Boris Yeltsin    71     89     98          4  
        2     USA     LEAD.v1-55                Lyndon Johnson    94     97     98          4  

. 
. */ Use this output to generate Appendix Table A.4 */
. 
. clear

. estimates clear

. 
. */ Table A.5 - Randomly Selected Leaders */
. 
. use WhyLeadersFightMonadicReplication.dta
(Why Leaders Fight - Monadic Replication)

. 
. */ Randomly Selected Leaders */
. 
. keep if treatment==1
(10565 observations deleted)

. 
. */ Initial leader risk score */
. */ excludes medicine, police, scienceeng, illegit from base model since all predict failure perfectly in this pool of 
> leaders */
. logit cwinit milnoncombat combat rebel warwin warloss rebelwin rebelloss leveledu age teacher journalism law religion 
> activist careerpolitician creative business aristocratlandowner militarycareer bluecollar gender totalspouses married 
> marriedinpower divorced childtotal parstability royalty orphanbinary officetenure1000 yearssincemidinit y2 y3, robust 
> cluster(leaderid)

Iteration 0:   log pseudolikelihood = -502.45765  
Iteration 1:   log pseudolikelihood = -359.46131  
Iteration 2:   log pseudolikelihood = -339.15112  
Iteration 3:   log pseudolikelihood = -337.16488  
Iteration 4:   log pseudolikelihood = -336.81626  
Iteration 5:   log pseudolikelihood = -336.80568  
Iteration 6:   log pseudolikelihood = -336.80564  
Iteration 7:   log pseudolikelihood = -336.80564  

Logistic regression                               Number of obs   =       1003
                                                  Wald chi2(33)   =     391.30
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -336.80564                 Pseudo R2       =     0.3297

                                    (Std. Err. adjusted for 124 clusters in leaderid)
-------------------------------------------------------------------------------------
                    |               Robust
             cwinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
       milnoncombat |   .9372624   .4160343     2.25   0.024     .1218502    1.752675
             combat |   .3409712   .4366082     0.78   0.435    -.5147652    1.196708
              rebel |   .0266497   .3787862     0.07   0.944    -.7157576     .769057
             warwin |  -1.139822   .5699228    -2.00   0.046     -2.25685   -.0227938
            warloss |  -1.044091   .8615318    -1.21   0.226    -2.732663      .64448
           rebelwin |   .6445224   .4358324     1.48   0.139    -.2096935    1.498738
          rebelloss |     .44893   .4782517     0.94   0.348    -.4884262    1.386286
           leveledu |   .4604613   .1855108     2.48   0.013     .0968668    .8240557
                age |   .0346524   .0113152     3.06   0.002     .0124751    .0568297
            teacher |  -.1007587   .5537181    -0.18   0.856    -1.186026    .9845088
         journalism |  -.6590885   .8808522    -0.75   0.454    -2.385527     1.06735
                law |   .0216695   .3906587     0.06   0.956    -.7440074    .7873464
           religion |  -.9321417   .6053345    -1.54   0.124    -2.118576    .2542922
           activist |   .4428247   .3192284     1.39   0.165    -.1828516    1.068501
   careerpolitician |  -.2556575   .2312705    -1.11   0.269    -.7089394    .1976243
           creative |  -1.111908   .4110512    -2.71   0.007    -1.917553   -.3062621
           business |  -.4641708   1.074408    -0.43   0.666    -2.569973    1.641631
aristocratlandowner |  -1.656815   .4395273    -3.77   0.000    -2.518272    -.795357
     militarycareer |   .2733611   .4309232     0.63   0.526    -.5712329    1.117955
         bluecollar |   -.581803   .4442026    -1.31   0.190    -1.452424    .2888181
             gender |  -1.772627    .968589    -1.83   0.067    -3.671026    .1257728
       totalspouses |  -.1087633    .204192    -0.53   0.594    -.5089722    .2914456
            married |  -.9014372   1.323741    -0.68   0.496    -3.495922    1.693048
     marriedinpower |      .4597   .8147023     0.56   0.573    -1.137087    2.056487
           divorced |   .6786493   .6373666     1.06   0.287    -.5705663    1.927865
         childtotal |   .0012089   .0037947     0.32   0.750    -.0062286    .0086464
       parstability |   .6055734   .5314154     1.14   0.254    -.4359817    1.647128
            royalty |   1.626288   .5083536     3.20   0.001     .6299332    2.622643
       orphanbinary |   -.028489   .4109765    -0.07   0.945    -.8339882    .7770102
   officetenure1000 |   .0109054   .0494046     0.22   0.825    -.0859259    .1077367
  yearssincemidinit |   -.322295   .0678595    -4.75   0.000    -.4552973   -.1892927
                 y2 |       .013   .0047466     2.74   0.006     .0036967    .0223032
                 y3 |  -.0001668   .0000853    -1.96   0.050    -.0003339    3.63e-07
              _cons |  -1.101015   1.467595    -0.75   0.453    -3.977449    1.775418
-------------------------------------------------------------------------------------

. lstat

Logistic model for cwinit

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        98            40  |        138
     -     |       103           762  |        865
-----------+--------------------------+-----------
   Total   |       201           802  |       1003

Classified + if predicted Pr(D) >= .5
True D defined as cwinit != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   48.76%
Specificity                     Pr( -|~D)   95.01%
Positive predictive value       Pr( D| +)   71.01%
Negative predictive value       Pr(~D| -)   88.09%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)    4.99%
False - rate for true D         Pr( -| D)   51.24%
False + rate for classified +   Pr(~D| +)   28.99%
False - rate for classified -   Pr( D| -)   11.91%
--------------------------------------------------
Correctly classified                        85.74%
--------------------------------------------------

. predict leaderrisk if e(sample)
(option pr assumed; Pr(cwinit))
(2 missing values generated)

. la var leaderrisk "Leader Attribute Risk Score"

. 
. */ Initial system risk score */
. 
. logit cwinit cinc dem aut syscon irregular tau_lead fiveyearchallengelag lastwarwin lastwarloss lastwardraw yearssince
> midinit y2 y3, robust cluster(ccode)

Iteration 0:   log pseudolikelihood = -490.02044  
Iteration 1:   log pseudolikelihood = -363.38919  
Iteration 2:   log pseudolikelihood = -341.25432  
Iteration 3:   log pseudolikelihood = -338.88568  
Iteration 4:   log pseudolikelihood = -338.25567  
Iteration 5:   log pseudolikelihood = -338.20352  
Iteration 6:   log pseudolikelihood = -338.20327  
Iteration 7:   log pseudolikelihood = -338.20327  

Logistic regression                               Number of obs   =        991
                                                  Wald chi2(13)   =     281.84
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -338.20327                 Pseudo R2       =     0.3098

                                         (Std. Err. adjusted for 78 clusters in ccode)
--------------------------------------------------------------------------------------
                     |               Robust
              cwinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
                cinc |   5.781656   2.299645     2.51   0.012     1.274434    10.28888
                 dem |  -.9547638   .5734849    -1.66   0.096    -2.078774    .1692459
                 aut |  -.0150333   .2911684    -0.05   0.959    -.5857129    .5556463
              syscon |  -2.068084   2.337709    -0.88   0.376    -6.649909    2.513742
           irregular |   .2283451   .8985049     0.25   0.799    -1.532692    1.989382
            tau_lead |   .1032579   .3434925     0.30   0.764    -.5699749    .7764908
fiveyearchallengelag |   .7053147   .2768006     2.55   0.011     .1627955    1.247834
          lastwarwin |   1.349346   .3338794     4.04   0.000     .6949548    2.003738
         lastwarloss |   1.334997   .3119173     4.28   0.000     .7236501    1.946343
         lastwardraw |   1.345168   .3569276     3.77   0.000     .6456024    2.044733
   yearssincemidinit |  -.3463333    .075533    -4.59   0.000    -.4943752   -.1982914
                  y2 |   .0156014   .0050963     3.06   0.002     .0056128    .0255899
                  y3 |  -.0002119   .0000897    -2.36   0.018    -.0003877   -.0000361
               _cons |  -1.344421   .6656641    -2.02   0.043    -2.649099   -.0397432
--------------------------------------------------------------------------------------
Note: 4 failures and 0 successes completely determined.

. lstat

Logistic model for cwinit

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |       101            55  |        156
     -     |        93           742  |        835
-----------+--------------------------+-----------
   Total   |       194           797  |        991

Classified + if predicted Pr(D) >= .5
True D defined as cwinit != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   52.06%
Specificity                     Pr( -|~D)   93.10%
Positive predictive value       Pr( D| +)   64.74%
Negative predictive value       Pr(~D| -)   88.86%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)    6.90%
False - rate for true D         Pr( -| D)   47.94%
False + rate for classified +   Pr(~D| +)   35.26%
False - rate for classified -   Pr( D| -)   11.14%
--------------------------------------------------
Correctly classified                        85.07%
--------------------------------------------------

. predict systemrisk if e(sample)
(option pr assumed; Pr(cwinit))
(14 missing values generated)

. la var systemrisk "System Risk Score"

. 
. */ generate MID initiation summary data for creation upon data collapse */
. gen cwinit_sum = cwinit

. la var cwinit_sum "Number of MID Initiations"

. 
. collapse (mean) leaderrisk systemrisk (sum) cwinit_sum (min) ccode inyear (max) outyear, by(leaderid leadername idacr)

. 
. */ List all leaders in this pool 
. gsort -leaderid

. gsort -cwinit_sum

. 
. xtile pct = leaderrisk, nq(100)

. xtile pct2 = systemrisk, nq(100)

. xtile pct3 = cwinit_sum, nq(100)

. replace pct=pct-1
(124 real changes made)

. replace pct2=pct2-1
(125 real changes made)

. replace pct3=pct3-1
(125 real changes made)

. list ccode idacr leaderid leadername pct pct2 pct3 cwinit_sum if cwinit_sum>=2, table clean noobs

    ccode   idacr       leaderid                   leadername   pct   pct2   pct3   cwinit~m  
      365     RUS   LEAD.v1-5476                 Josef Stalin    96     95     99         38  
      365     RUS   LEAD.v1-5470                  Nicholas II    95     94     98         24  
      600     MOR   LEAD.v1-7138                    Hassan II    77     77     97         13  
      235     POR   LEAD.v1-4279               Caetano Veloso    88     93     96          9  
      710     CHN   LEAD.v1-7858                  Jiang Zemin    91     97     95          8  
      800     THI   LEAD.v1-8593          Thanon Kittakachorn    90     90     95          8  
      713     TAW   LEAD.v1-7930                 Lee Teng-Hui    89     80     93          7  
      731     PRK   LEAD.v1-7945                  Kim Jong-Il    97     91     93          7  
      816     DRV   LEAD.v1-8740                      Le Duan    79     84     92          6  
      651     EGY   LEAD.v1-7423                  Anwar Sadat    85     89     92          6  
      540     ANG   LEAD.v1-6865      Jose Eduardo dos Santos    67     72     90          5  
      651     EGY   LEAD.v1-7414                     Farouk I    62     71     90          5  
      670     SAU   LEAD.v1-7585   Saud bin Abdulaziz Al Saud    79     76     87          4  
      365     RUS   LEAD.v1-5494            Mikhail Gorbachev    75     96     87          4  
      365     RUS   LEAD.v1-5488                Yuri Andropov    87     99     87          4  
      710     CHN   LEAD.v1-7852                  Hua Guofeng    94     96     87          4  
      670     SAU   LEAD.v1-7594   Fahd bin Abdulaziz Al Saud    58     82     84          3  
      645     IRQ   LEAD.v1-7378                  Abd al-Ilah    50     57     84          3  
      501     KEN   LEAD.v1-6739              Daniel Arap Moi    59     46     84          3  
      530     ETH   LEAD.v1-6835               Haile Selassie    81     67     78          2  
      645     IRQ   LEAD.v1-7390            Abdul Rahman Arif    98     85     78          2  
      325     ITA   LEAD.v1-4654             Francesco Crispi    93     88     78          2  
      355     BUL   LEAD.v1-5362             Valko Chervenkov    63     74     78          2  
      365     RUS   LEAD.v1-5491         Konstantin Chernenko    92     98     78          2  
      390     DEN   LEAD.v1-6145               Viggo Kampmann    54     63     78          2  
      360     RUM   LEAD.v1-5410                  Ferdinand I    62     66     78          2  
      640     TUR   LEAD.v1-7285                  Ismet Inonu    75     80     78          2  

. 
. */ Use this output to generate Appendix Table A.5 */
. 
. clear

. 
. */ Tables A.6 and A.7 - Coarsened Exact Matching */
. 
. */ Demonstrating consistency of the results when using matching: footnote 66, p. 127 */
. 
. use WhyLeadersFightMonadicReplication.dta, clear
(Why Leaders Fight - Monadic Replication)

. drop if milservice==.
(25 observations deleted)

. 
. */ Matching on system variables */
. 
. */ Use military service as treatment because if it is country-level attributes driving a selection process, it would b
> e attributes that mean it is the general conflict propensity of countries */
. */ Not variables like national military service, driving the outcomes */
. 
. imb cinc dem aut tau_lead irregular fiveyearchallengelag, treatment(milservice)

Multivariate L1 distance: .51772871

Univariate imbalance:

                           L1     mean      min      25%      50%      75%      max
                cinc   .08305   .00604  5.8e-06  3.7e-05   .00086   .00177    .0025
                 dem   .19345  -.19345        0        0        0       -1        0
                 aut   .19004   .19004        0        0        0        1        0
            tau_lead    .1921  -.05013  -.01379   -.0543  -.00596  -.26454        0
           irregular   .28793   .28793        0        0        0        1        0
fiveyearchallengelag   .05033   .05033        0        0        0        0        0

. cem cinc dem aut tau_lead irregular fiveyearchallengelag, treatment(milservice)
(using the  break method for imbalance)

Matching Summary:
-----------------
Number of strata: 342
Number of matched strata: 176

              0     1
      All  7243  4302
  Matched  6876  3875
Unmatched   367   427


Multivariate L1 distance: .31032605

Univariate imbalance:

                            L1      mean       min       25%       50%       75%       max
                cinc    .09366    .00095   5.8e-06  -2.0e-05    .00071    .00128    .00125
                 dem   1.6e-15   8.0e-16         0         0         0         0         0
                 aut   9.9e-15   1.1e-14         0         0         0         0         0
            tau_lead    .06288    .00129   -.01379    .00152   -.00227   -.01303         .
           irregular   1.1e-14   6.9e-15         0         0         0         0         0
fiveyearchallengelag   1.0e-14   7.7e-15         0         0         0         0         0

. 
. quietly logit cwinit milservice rebel warwin warloss rebelwin rebelloss leveledu age teacher journalism law medicine r
> eligion activist careerpolitician creative business aristocratlandowner police militarycareer scienceeng bluecollar ge
> nder totalspouses married marriedinpower divorced childtotal parstability illegit royalty orphanbinary officetenure100
> 0 yearssincemidinit y2 y3 [iweight=cem_weights], robust cluster(leaderid)

. estimates store m1

. predict leaderrisk if e(sample)
(option pr assumed; Pr(cwinit))
(819 missing values generated)

. la var leaderrisk "Leader Attribute Risk Score"

. 
. esttab m1 using AppendixTableA_6.rtf, replace onecell se pr2 t(3) b(a3) scalars(ll) legend label collabels(none) varla
> bels(_cons Constant) star(* 0.10 ** 0.05 *** 0.01) mtitles("Leader Risk Model: Coarsened Exact Matching")
(output written to AppendixTableA_6.rtf)

. 
. */ Initial system risk score */
. 
. logit cwinit cinc dem aut syscon irregular tau_lead fiveyearchallengelag lastwarwin lastwarloss lastwardraw yearssince
> midinit y2 y3, robust cluster(ccode)

Iteration 0:   log pseudolikelihood = -5499.2198  
Iteration 1:   log pseudolikelihood = -4384.9063  
Iteration 2:   log pseudolikelihood = -4237.9167  
Iteration 3:   log pseudolikelihood = -4229.6668  
Iteration 4:   log pseudolikelihood = -4229.5981  
Iteration 5:   log pseudolikelihood = -4229.5979  

Logistic regression                               Number of obs   =      11365
                                                  Wald chi2(13)   =     779.88
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -4229.5979                 Pseudo R2       =     0.2309

                                        (Std. Err. adjusted for 178 clusters in ccode)
--------------------------------------------------------------------------------------
                     |               Robust
              cwinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
                cinc |   9.408252   2.027485     4.64   0.000     5.434454    13.38205
                 dem |  -.2393729    .158888    -1.51   0.132    -.5507877     .072042
                 aut |   .1683024   .1300593     1.29   0.196    -.0866092    .4232139
              syscon |  -3.477303   1.067494    -3.26   0.001    -5.569554   -1.385052
           irregular |   .2103088    .116508     1.81   0.071    -.0180427    .4386603
            tau_lead |  -.0452534   .1618575    -0.28   0.780    -.3624882    .2719814
fiveyearchallengelag |   .6588187   .0783936     8.40   0.000       .50517    .8124673
          lastwarwin |   .7326559   .1532621     4.78   0.000     .4322677    1.033044
         lastwarloss |   .6446885    .132169     4.88   0.000     .3856421    .9037349
         lastwardraw |   .9285214   .2115827     4.39   0.000     .5138269    1.343216
   yearssincemidinit |  -.2685533   .0238126   -11.28   0.000    -.3152252   -.2218813
                  y2 |   .0092635   .0013308     6.96   0.000     .0066551    .0118719
                  y3 |  -.0000886   .0000181    -4.90   0.000     -.000124   -.0000531
               _cons |  -.7268791   .3388905    -2.14   0.032    -1.391092   -.0626659
--------------------------------------------------------------------------------------

. predict systemrisk if e(sample)
(option pr assumed; Pr(cwinit))
(180 missing values generated)

. la var systemrisk "System Risk Score"

. 
. */ generate MID initiation summary data for creation upon data collapse */
. gen cwinit_sum = cwinit

. la var cwinit_sum "Number of MID Initiations"

. 
. collapse (mean) leaderrisk systemrisk (sum) cwinit_sum (min) ccode inyear (max) outyear, by(leaderid leadername idacr)

. 
. gsort -leaderid

. gsort -cwinit_sum

. 
. xtile pct = leaderrisk, nq(100)

. xtile pct3 = cwinit_sum, nq(100)

. replace pct=pct-1
(2187 real changes made)

. replace pct3=pct3-1
(2269 real changes made)

. list ccode idacr leaderid leadername pct cwinit_sum if pct3>=98, table clean noobs

    ccode   idacr       leaderid                 leadername   pct   cwinit~m  
      630     IRN   LEAD.v1-7252         Ayatollah Khomeini    99         64  
      255     GMY   LEAD.v1-4354               Adolf Hitler    97         52  
      710     CHN   LEAD.v1-7849                 Mao Zedong    99         45  
      645     IRQ   LEAD.v1-7396             Saddam Hussein    97         44  
      365     RUS   LEAD.v1-5476               Josef Stalin    98         38  
      365     RUS   LEAD.v1-5485            Leonid Brezhnev    98         31  
      325     ITA   LEAD.v1-4720           Benito Mussolini    98         27  
      365     RUS   LEAD.v1-5482          Nikita Khrushchev    98         26  
      255     GMY   LEAD.v1-4306                 Wilhelm II    78         25  
      365     RUS   LEAD.v1-5470                Nicholas II    84         24  
      365     RUS   LEAD.v1-5497              Boris Yeltsin    85         23  
      731     PRK   LEAD.v1-7942                Kim Il-Sung    97         21  
      490     DRC   LEAD.v1-6703           Mobutu Sese Seko    86         17  
      750     IND   LEAD.v1-8212           Jawaharlal Nehru    89         17  
      713     TAW   LEAD.v1-7921            Chiang Kai-shek    92         17  
        2     USA     LEAD.v1-67              Ronald Reagan    99         16  
      710     CHN   LEAD.v1-7855              Deng Xiaoping    99         16  
      620     LIB   LEAD.v1-7186            Muammar Qaddafi    88         16  
      652     SYR   LEAD.v1-7474             Hafez Al-Assad    90         16  
        2     USA     LEAD.v1-73            William Clinton    81         15  
      305     AUH   LEAD.v1-4438           Francis Joseph I    67         13  
      770     PAK   LEAD.v1-8317                  Ayub Khan     .         13  
      850     INS   LEAD.v1-8860                    Sukarno    95         13  
      600     MOR   LEAD.v1-7138                  Hassan II    94         13  
      651     EGY   LEAD.v1-7420         Gamal Abdel Nasser    97         13  
      552     ZIM   LEAD.v1-6883                  Ian Smith    85         12  
      530     ETH   LEAD.v1-6850           Mengistu Marriam    96         12  
      560     SAF   LEAD.v1-6931                Louis Botha    81         12  
      500     UGA   LEAD.v1-6715                   Idi Amin    98         11  
      345     YUG   LEAD.v1-5029         Slobodan Milosevic    93         11  
      200     UKG   LEAD.v1-2818   Salisbury (3rd Marquess)    93         10  
      220     FRN   LEAD.v1-3613         Francois Mitterand    95         10  
      200     UKG   LEAD.v1-2851        Neville Chamberlain    81         10  
        2     USA     LEAD.v1-49          Dwight Eisenhower    97         10  
      365     RUS   LEAD.v1-5473             Vladimir Lenin    87         10  
        2     USA     LEAD.v1-31             Woodrow Wilson    83         10  
      200     UKG   LEAD.v1-2854          Winston Churchill    92          9  
      630     IRN   LEAD.v1-7258   Akbar Hashemi Rafsanjani    88          9  
       40     CUB    LEAD.v1-211               Fidel Castro    73          9  
      652     SYR   LEAD.v1-7438             Abid Shishakli    93          9  
      235     POR   LEAD.v1-4279             Caetano Veloso    83          9  
      640     TUR   LEAD.v1-7336                Turgut Ozal    88          9  

. 
. */ Use this output to generate Table A.7 */
. 
. estimates clear

. clear

. 
. */ Table A.9: Childhood War */
. use "Figure5_1.dta", clear
(Why Leaders Fight - Figure 5.1)

. 
. logit binarymid milservice war_exp, robust cluster(leaderid)

Iteration 0:   log pseudolikelihood = -1307.4933  
Iteration 1:   log pseudolikelihood = -1286.8249  
Iteration 2:   log pseudolikelihood = -1286.7505  
Iteration 3:   log pseudolikelihood = -1286.7505  

Logistic regression                               Number of obs   =       2107
                                                  Wald chi2(2)    =      42.24
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -1286.7505                 Pseudo R2       =     0.0159

                            (Std. Err. adjusted for 2104 clusters in leaderid)
------------------------------------------------------------------------------
             |               Robust
   binarymid |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  milservice |   .5572718   .1007463     5.53   0.000     .3598127     .754731
     war_exp |   .2606388   .0963003     2.71   0.007     .0718937    .4493839
       _cons |  -1.089692   .0705219   -15.45   0.000    -1.227913   -.9514719
------------------------------------------------------------------------------

. estimates store m1

. 
. esttab m1 using AppendixTableA_9.rtf, replace onecell se pr2 t(3) b(a3) scalars(ll) legend label collabels(none) varla
> bels(_cons Constant) star(* 0.10 ** 0.05 *** 0.01) mtitles("Childhood War Model")
(output written to AppendixTableA_9.rtf)

. 
. */ RESULTS ARE CONSISTENT WITH UPDATED MONADIC FILE */
. 
. use WhyLeadersFightMonadicReplication_updated.dta, clear
(Why Leaders Fight - Monadic Replication - With updated LEAD data)

. 
. */ Initial leader risk score */
. 
. logit cwinit milnoncombat combat rebel warwin warloss rebelwin rebelloss leveledu age teacher journalism law medicine 
> religion activist careerpolitician creative business aristocratlandowner police militarycareer scienceeng bluecollar g
> ender totalspouses married marriedinpower divorced childtotal parstability illegit royalty orphanbinary officetenure10
> 00 yearssincemidinit y2 y3, robust cluster(leaderid)

Iteration 0:   log pseudolikelihood = -5032.1132  
Iteration 1:   log pseudolikelihood = -4153.1481  
Iteration 2:   log pseudolikelihood =  -4049.387  
Iteration 3:   log pseudolikelihood =  -4047.344  
Iteration 4:   log pseudolikelihood = -4047.3344  
Iteration 5:   log pseudolikelihood = -4047.3344  

Logistic regression                               Number of obs   =      10070
                                                  Wald chi2(37)   =     546.59
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -4047.3344                 Pseudo R2       =     0.1957

                                   (Std. Err. adjusted for 1911 clusters in leaderid)
-------------------------------------------------------------------------------------
                    |               Robust
             cwinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
       milnoncombat |    .710867   .1974608     3.60   0.000     .3238509    1.097883
             combat |   .4255282   .1817189     2.34   0.019     .0693658    .7816906
              rebel |  -.0001043   .1398834    -0.00   0.999    -.2742708    .2740622
             warwin |   .2342174   .1721812     1.36   0.174    -.1032516    .5716864
            warloss |   .0848887   .1820192     0.47   0.641    -.2718624    .4416398
           rebelwin |  -.0012112   .1547363    -0.01   0.994    -.3044887    .3020663
          rebelloss |   .7392549   .2765666     2.67   0.008     .1971943    1.281315
           leveledu |   .0251578   .0694515     0.36   0.717    -.1109646    .1612802
                age |   .0075882   .0044532     1.70   0.088      -.00114    .0163164
            teacher |   .0540248   .1370645     0.39   0.693    -.2146167    .3226663
         journalism |  -.1059979    .220415    -0.48   0.631    -.5380035    .3260076
                law |  -.1264175   .1389479    -0.91   0.363    -.3987504    .1459154
           medicine |  -.5978347   .2590137    -2.31   0.021    -1.105492   -.0901772
           religion |   .6121762   .4661248     1.31   0.189    -.3014117    1.525764
           activist |   .1292996   .1376397     0.94   0.348    -.1404692    .3990684
   careerpolitician |   .0067949   .1075091     0.06   0.950    -.2039191    .2175088
           creative |   .5064943   .2646774     1.91   0.056    -.0122638    1.025252
           business |  -.1001443   .1578843    -0.63   0.526    -.4095919    .2093033
aristocratlandowner |   -.284875   .2216589    -1.29   0.199    -.7193184    .1495685
             police |   .3732309   .4929406     0.76   0.449    -.5929149    1.339377
     militarycareer |  -.2224034    .202348    -1.10   0.272    -.6189983    .1741914
         scienceeng |   .2189234   .2394694     0.91   0.361     -.250428    .6882749
         bluecollar |   .0842809   .2085053     0.40   0.686    -.3243821    .4929438
             gender |   -.371256    .286149    -1.30   0.194    -.9320977    .1895857
       totalspouses |  -.0275771    .040534    -0.68   0.496    -.1070223     .051868
            married |   .1260575   .3496912     0.36   0.718    -.5593246    .8114395
     marriedinpower |   -.187387   .1952358    -0.96   0.337    -.5700421    .1952681
           divorced |  -.0977847   .1212206    -0.81   0.420    -.3353726    .1398032
         childtotal |   .0040894   .0117229     0.35   0.727     -.018887    .0270658
       parstability |   .3635487   .2036868     1.78   0.074      -.03567    .7627674
            illegit |   -.541192   .2426269    -2.23   0.026    -1.016732    -.065652
            royalty |  -.2033984   .2164529    -0.94   0.347    -.6276383    .2208414
       orphanbinary |  -.1814158   .2688211    -0.67   0.500    -.7082955    .3454639
   officetenure1000 |   .0270489   .0152049     1.78   0.075    -.0027521      .05685
  yearssincemidinit |  -.3332736   .0227604   -14.64   0.000    -.3778832    -.288664
                 y2 |   .0106935   .0012799     8.36   0.000      .008185     .013202
                 y3 |  -.0000947   .0000176    -5.39   0.000    -.0001291   -.0000602
              _cons |  -.6477987   .5237122    -1.24   0.216    -1.674256    .3786584
-------------------------------------------------------------------------------------

. predict leaderrisk if e(sample)
(option pr assumed; Pr(cwinit))
(1500 missing values generated)

. la var leaderrisk "Leader Attribute Risk Score"

. 
. */ Initial system risk score */
. 
. logit cwinit cinc dem aut syscon irregular tau_lead fiveyearchallengelag lastwarwin lastwarloss lastwardraw yearssince
> midinit y2 y3, robust cluster(ccode)

Iteration 0:   log pseudolikelihood =  -5505.478  
Iteration 1:   log pseudolikelihood = -4388.0857  
Iteration 2:   log pseudolikelihood = -4239.6206  
Iteration 3:   log pseudolikelihood = -4230.9585  
Iteration 4:   log pseudolikelihood = -4230.8574  
Iteration 5:   log pseudolikelihood = -4230.8571  

Logistic regression                               Number of obs   =      11388
                                                  Wald chi2(13)   =     785.87
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -4230.8571                 Pseudo R2       =     0.2315

                                        (Std. Err. adjusted for 178 clusters in ccode)
--------------------------------------------------------------------------------------
                     |               Robust
              cwinit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
                cinc |   9.404467   2.027024     4.64   0.000     5.431572    13.37736
                 dem |  -.2396804   .1588788    -1.51   0.131     -.551077    .0717163
                 aut |   .1679045   .1299729     1.29   0.196    -.0868377    .4226466
              syscon |  -3.486719   1.066383    -3.27   0.001    -5.576791   -1.396647
           irregular |   .2121851   .1162016     1.83   0.068    -.0155659    .4399361
            tau_lead |  -.0436213   .1617221    -0.27   0.787    -.3605907    .2733482
fiveyearchallengelag |   .6592767   .0783817     8.41   0.000     .5056514     .812902
          lastwarwin |   .7341308   .1532903     4.79   0.000     .4336873    1.034574
         lastwarloss |   .6475341   .1319239     4.91   0.000      .388968    .9061001
         lastwardraw |    .929418    .211505     4.39   0.000     .5148757     1.34396
   yearssincemidinit |  -.2690812   .0237309   -11.34   0.000    -.3155929   -.2225696
                  y2 |   .0092909    .001325     7.01   0.000     .0066941    .0118878
                  y3 |  -.0000889    .000018    -4.94   0.000    -.0001242   -.0000536
               _cons |  -.7252537   .3385474    -2.14   0.032    -1.388794   -.0617129
--------------------------------------------------------------------------------------

. predict systemrisk if e(sample)
(option pr assumed; Pr(cwinit))
(182 missing values generated)

. la var systemrisk "System Risk Score"

. 
. */ generate MID initiation summary data for creation upon data collapse */
. gen cwinit_sum = cwinit

. la var cwinit_sum "Number of MID Initiations"

. 
. collapse (mean) leaderrisk systemrisk (sum) cwinit_sum (min) ccode inyear (max) outyear, by(leaderid leadername idacr)

. 
. */ List top 35 conflict prone leaders, by leader risk score */
. gsort -leaderrisk

. 
. list ccode idacr leaderid leadername leaderrisk systemrisk cwinit_sum in 1/35, table clean noobs

    ccode   idacr       leaderid             leadername   leader~k   system~k   cwinit~m  
      630     IRN   LEAD.v1-7252     Ayatollah Khomeini   .7304118   .5692552         64  
      710     CHN   LEAD.v1-7855          Deng Xiaoping   .6990601   .7410197         16  
      344     CRO   LEAD.v1-4975         Franjo Tudjman   .6898885   .2393584          5  
      560     SAF   LEAD.v1-6904              Jan Smuts   .6676258   .0971961          0  
        2     USA     LEAD.v1-67          Ronald Reagan   .6566846   .6556708         16  
      645     IRQ   LEAD.v1-7393         Hassan Al-Bakr   .6469395   .4540481          8  
      710     CHN   LEAD.v1-7852            Hua Guofeng   .6428473   .7426027          4  
      710     CHN   LEAD.v1-7849             Mao Zedong   .6397166   .7220504         45  
      220     FRN   LEAD.v1-3532        Georges Bidault   .6391577   .3106591          0  
        2     USA      LEAD.v1-7               Garfield   .6356922   .4602128          0  
      135     PER   LEAD.v1-1843             A. Caceres   .6023871   .2388555          0  
        2     USA     LEAD.v1-52        John F. Kennedy   .5898399   .8063945          2  
        2     USA     LEAD.v1-25     Theodore Roosevelt   .5845297   .6240175          7  
        2     USA     LEAD.v1-61            Gerald Ford   .5761978   .6619125          4  
      220     FRN   LEAD.v1-3592               Gaillard   .5750967   .3012592          1  
      365     RUS   LEAD.v1-5476           Josef Stalin   .5702586   .6835461         38  
      366     EST   LEAD.v1-5503                   Pats   .5656836   .2161491          0  
      365     RUS   LEAD.v1-5485        Leonid Brezhnev   .5619475   .8114074         31  
      365     RUS   LEAD.v1-5491   Konstantin Chernenko   .5603455   .8005411          2  
      365     RUS   LEAD.v1-5482      Nikita Khrushchev   .5566957   .7972519         26  
      220     FRN   LEAD.v1-3589        Bourges-Maunory   .5534626   .4475837          0  
      666     ISR   LEAD.v1-7531             Ben Gurion   .5503228   .2887749          7  
      367     LAT   LEAD.v1-5593                Ulmanis   .5430443    .254904          0  
      552     ZIM   LEAD.v1-6886               Muzorewa   .5364338   .2786274          0  
      255     GMY   LEAD.v1-4354           Adolf Hitler   .5301858   .7174438         52  
      666     ISR   LEAD.v1-7540                 Eshkol   .5226212   .3292592          4  
        2     USA     LEAD.v1-10              C. Arthur   .5195452   .6037325          2  
      666     ISR   LEAD.v1-7537             Ben Gurion   .5166255    .302938          4  
      666     ISR   LEAD.v1-7561                  Peres   .5145993   .3392836          2  
      645     IRQ   LEAD.v1-7396         Saddam Hussein   .5104818   .5062273         44  
      666     ISR   LEAD.v1-7546             Golda Meir   .5078636   .3390384          3  
        2     USA     LEAD.v1-55         Lyndon Johnson   .5074853   .7782919          5  
      475     NIG   LEAD.v1-6622               Obasanjo    .505722   .2814909          1  
      651     EGY   LEAD.v1-7420     Gamal Abdel Nasser    .504764   .4627729         13  
        2     USA     LEAD.v1-70                   Bush   .5008501   .7060421          5  

. 
. */ List top 2% of most dangerous leaders in reality, and their military dispute initiations, comparing how leader mode
> l v. system model predict their risk % */
. gsort -leaderid

. gsort -cwinit_sum

. 
. xtile pct = leaderrisk, nq(100)

. xtile pct2 = systemrisk, nq(100)

. xtile pct3 = cwinit_sum, nq(100)

. replace pct=pct-1
(1911 real changes made)

. replace pct2=pct2-1
(2258 real changes made)

. replace pct3=pct3-1
(2282 real changes made)

. list ccode idacr leaderid leadername pct pct2 pct3 cwinit_sum if pct3>=99, table clean noobs

    ccode   idacr       leaderid           leadername   pct   pct2   pct3   cwinit~m  
      630     IRN   LEAD.v1-7252   Ayatollah Khomeini    99     97     99         64  
      255     GMY   LEAD.v1-4354         Adolf Hitler    98     99     99         52  
      710     CHN   LEAD.v1-7849           Mao Zedong    99     99     99         45  
      645     IRQ   LEAD.v1-7396       Saddam Hussein    98     96     99         44  
      365     RUS   LEAD.v1-5476         Josef Stalin    99     98     99         38  
      365     RUS   LEAD.v1-5485      Leonid Brezhnev    99     99     99         31  
      325     ITA   LEAD.v1-4720     Benito Mussolini    97     93     99         27  
      365     RUS   LEAD.v1-5482    Nikita Khrushchev    98     99     99         26  
      255     GMY   LEAD.v1-4306           Wilhelm II    94     96     99         25  
      365     RUS   LEAD.v1-5470          Nicholas II    95     97     99         24  
      365     RUS   LEAD.v1-5497        Boris Yeltsin    91     97     99         23  
      731     PRK   LEAD.v1-7942          Kim Il-Sung    96     90     99         21  
      713     TAW   LEAD.v1-7921      Chiang Kai-shek    94     83     99         17  
      750     IND   LEAD.v1-8212     Jawaharlal Nehru    90     92     99         17  
      490     DRC   LEAD.v1-6703     Mobutu Sese Seko    69     88     99         17  
      710     CHN   LEAD.v1-7855        Deng Xiaoping    99     99     99         16  
      652     SYR   LEAD.v1-7474       Hafez Al-Assad    94     89     99         16  
      620     LIB   LEAD.v1-7186      Muammar Qaddafi    91     88     99         16  
        2     USA     LEAD.v1-67        Ronald Reagan    99     98     99         16  
        2     USA     LEAD.v1-73      William Clinton    82     98     99         15  

. 
. */ CLOSE LOG FILE */
. 
. log close
      name:  <unnamed>
       log:  
  log type:  text
 closed on:   1 Dec 2015, 13:19:05
------------------------------------------------------------------------------------------------------------------------
